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This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error. [ABSTRACT FROM AUTHOR]

ObjectiveOverreaction is a noticeable anomaly in financial markets that leads to various consequences, including market inefficiency. As such, one of the prominent topics investigated in major global stock exchanges is shareholders' overreaction. This phenomenon is particularly prevalent in emerging and less developed markets, where investors tend to overreact to financial events. Overreaction, as a behavioral bias, distorts investors' decision-making in uncertain conditions, pulling the market away from its efficient state. Predicting and identifying the persistence of these reactions can assist investors in making more rational decisions regarding the purchase or sale of shares and other securities. Therefore, predicting and identifying the continuation of shareholders' overreaction trends can serve as a valuable tool for investors, financial analysts, and investment managers to make decisions based on intuition and precise analysis. To create an effective predictive model, various methods, such as regression analysis, can be employed to analyze the relationship between different variables and the continuation of shareholders' overreaction trends. Additionally, artificial neural networks (ANNs), as an advanced method, can be used to model the non-linear complexities and intricate connections between variables. This topic is directly related to predicting shareholders' behavior and investment decision-making, ultimately helping improve investment strategies and risk management. Hence, the main objective of this research is to conduct a comparative analysis of artificial neural networks and linear regression in predicting the continuation of shareholders' overreaction trends.MethodsThe present study is descriptive-causal, utilizing ex post facto research. To test the research hypotheses, multivariate linear regression based on panel data and a combination of time series was employed. Data was collected using the library research method, and necessary information was gathered by studying the financial statements of companies within the statistical population. The statistical population includes all companies listed on the Tehran Stock Exchange between 2011 and 2021, with 110 companies selected through systematic elimination sampling. In data analysis, regression methods were used to examine the relationships between variables, and the results were compared with those obtained from artificial neural networks.ResultsThe results indicate the superiority of the artificial neural network model in terms of the coefficient of determination and the MSE (Mean Squared Error) index. Specifically, the highest coefficient of determination for the artificial neural network (with 1 hidden layer and 9 neurons) for test data is 0.3880, compared to 0.349 for the linear regression model. Moreover, the results show that the MSE for the artificial neural network (1 hidden layer and 9 neurons) for test data is 0.003266, compared to 0.004 for the linear regression model. Thus, similar to the coefficient of determination, the MSE index is also better in the case of the artificial neural network.ConclusionThe artificial neural network model is capable of uncovering complex and non-linear patterns, providing the most accurate predictions. By using this model, stock return trends can be predicted more precisely and reliably.

Predicting future landuse changes is an important step in the proper planning and management of watersheds. Therefore, in this study, the land use modeling of the Doroodzan Dam watershed was discussed. First, the land use maps of 1996, 2005, 2016 and 2021 were extracted using Envi software, and the maximum likelihood method. The transition potential maps were modeled for each of the sub-models by using multi-layer perceptron artificial neural network and 6 variables and Markov chain was used to calculate the allocation change to each land use. Then, the land use map for 2021 was predicted using Land Change Modeler (LCM) and 2005-2016 calibration period. To verify modeling accuracy, Hits 1.92%, Misses 8.8%, False Alarms 2.94% were calculated. The ratio of Hits to the total pixels has changed 14% indicates that model results are acceptable. Then, the land use map for the year 2050 was predicted. The results showed that given the predicted LC for year 2050 in comparison with the year 2021, bare land, agricultural land, residential areas and orchards will increase by 54290, 7621, 4494, and 2391 ha, respectively. Whereas grassland and forest cover will decrease 68441, and 689 ha, respectively.

Introduction Life on Earth is influenced by precipitation. Precipitation is one of the most significant factors that affect the hydrological cycle. Considering that precipitation is non-linear, complex, and can be changed according to spatial and temporal, estimating the amount of this important atmospheric factor in each month or year for each region and watershed is particularly important in managing and optimizing water resources. Various optimization models and algorithms have been proposed for modeling hydrological systems in recent decades. These algorithms have significantly reduced errors and increased accuracy. Still, since hydrological systems rely on random events, none of the methods can be completely and accurately selected as a superior model for modeling and estimating. The honey badger algorithm is an innovative algorithm that requires a few iterations to achieve an optimal solution, and this increases the speed of reaching the desired results. In current study investigates the performance of three models, including multiple linear regression (MLR), artificial neural network (ANN), and hybrid artificial neural network with honey badger optimization algorithm (HBA-ANN) for modeling the temporal and spatial precipitation in East Azarbaijan province. The best-developed model was selected by evaluation criteria such as R, RMSE, NRMSE, MBE, and NSE, the best model is selected. Materials and Methods The MLR model is one of the methods to analyze and investigate several variables. In this method, the model has one dependent variable and several independent variables, so that a linear equation is generated between the independent variables called X1, X2, ..., Xn and the dependent variable Y. ANN is a black box model of neural networks in the human brain. One of the most used methods is the BP method, which includes two stages. In the first stage, which is entitled feed-forward, the error value is calculated, after comparing output and objective values. In the second stage, which is labeled the back-propagation, the error value calculated in the previous step is corrected. The mentioned two stages continue until the output of the model approaches the desired output. The HBA is a new algorithm that simulates the honey-seeking behavior of a creature called the honey badger. The HBA includes two stages. In the first phase, the locations of this creature are calculated, and in the second phase, the exact distance between the HBA and the prey (dj) is calculated based on the honey intensity (S) and the honey smell intensity (Ij), as well as its new and optimal location for the prey Xnew. In the HBA-ANN model, the HBA algorithm is used to determine the most optimal output value in the ANN and increase performance in modeling. Therefore, the developed hybrid model can have the characteristics of both ANN and HBA methods. Results and Discussion In this study, in the first stage, the temporal modeling, and in the second stage, the spatial modeling of the monthly precipitation of 18 stations in East Azarbaijan province during the period of 1996-2022 using MLP, ANN, and HBA-ANN models has been paid. For temporal modeling of precipitation, one and two-month precipitation delay steps of the stations were used as input parameters. The first 70% of the dataset was selected for the training phase and the last 30% of the dataset was selected for the testing phase. Based on the results obtained from evaluation criteria and graphic diagrams, it can be concluded that the HBA-ANN model indicated significant accuracy compared to other models in the temporal modeling of precipitation. Also, by comparing the results of the stations in the HBA-ANN model, the Heris station with R =0.94, RMSE=2.25, NSE=0.79, NRMSE=0.04, and MBE=1.06 in the testing stage performed better compared with other stations. For spatial modeling of precipitation, the geographic coordinates of the stations, which include longitude, latitude, and altitude, are used as input parameters, and average monthly precipitation is used as the output parameter. From eighteen stations, 70% of the stations were selected for the training phase and 30% of the stations were selected for the testing phase. Based on the results obtained from R=0.95, RMSE=1.03, NSE =0.92, NRMSE = 0.03, and MBE = -0.81 and graphical diagrams, it can be concluded that the HBA-ANN model revealed significant accuracy compared to other models in spatial modeling of precipitation. Conclusion Precipitation is one of the most important factors that significantly change the hydrological cycle. Therefore, modeling and estimating this parameter is vital. In this study, the performance of multiple linear regression (MLR), artificial neural network (ANN), and hybrid ANN using honey badger algorithm (HBA-ANN) models were used for the spatial and temporal modeling of precipitation in East Azarbaijan province. For spatial modeling, the time delay steps of one and two months of station precipitation were selected as input parameters. Also, for temporal modeling, the longitude, latitude, and altitude parameters were used. The mentioned models were evaluated by R, RMSE, NSE, NRMSE, and MBE assessment criteria. According to the results of temporal modeling, the HBA-ANN model for all stations, especially Heris station with R equal to 0.94, RMSE equal to 2.25, NSE equal to 0.79, NRMSE equal to 0.04, and MBEequal to 1.06 is selected as the superior model. Also, based on the results obtained from spatial modeling, the HBA-ANN model with R equal to 0.95, RMSE equal to 1.03, NSE equal to 0.92, NRMSE equal to 0.03, and MBEequal to -0.81 was selected as the best model. The MLR and ANN models, respectively, presented a relatively poor performance compared to the developed hybrid model. [ABSTRACT FROM AUTHOR]

Forecasting monthly and seasonal rainfall is very important from a hydrological point of view, including the water resources management, as well as from climatic and atmospheric hazards perspective. Monthly and seasonal forecasts are widely used in sectors such as energy and agriculture. On the other hand, it has been proven that large-scale atmospheric teleconnections and indices have an effect on the atmospheric conditions of different parts of the earth. Machine learning methods are widely used to predict different atmospheric variables. The reason for using these methods is the high modeling ability of these algorithms when faced with a large amount of data. At the same time, these algorithms have many other capabilities such as the ability to model uncertainty. In this article, three machine learning methods, namely artificial neural network, gradient boosting and random forest are used for prediction. The performance of these algorithms will be compared with each other in monthly and seasonal forecasting scales. The studied area is a part of northwestern Iran, including the area of Lake Urmia basin. Three evaluation criteria, namely Nash-Sutcliffe, correlation coefficient and root mean square error were used to evaluate the results. Moreover, by using the Nino 3.4, WP, and NAO indices, the regional average monthly rainfall in the region is predicted with the help of three mentioned machine learning methods. The reason for choosing these three indicators is their effectiveness on the rainfall of the studied area. Two monthly and seasonal modeling methods are compared. Since teleconnections may be effective on precipitation with a delay, different delays of zero to three months were considered for all three studied teleconnections and the best results were obtained. Three methods of machine learning, i.e., random forest, neural network and gradient boosting were used. In the monthly mode, the performance of the gradient boosting method (with a three-month delay for the WP index and the other two indices without delay) was better than the random forest method, and the neural network ranked last in terms of performance. In terms of estimating the extreme precipitation, the gradient boosting method has an acceptable performance. In seasonal modeling, for the three seasons of spring (March-April-May), autumn (September-October-November) and winter (December-January-February), the performance of random forest and for the summer season (June-July-August), the performance of the gradient boosting method were superior to other methods. [ABSTRACT FROM AUTHOR]

This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For geochemical modeling two methods were utulized: 1) artificial neural network (ANN), 2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model. [ABSTRACT FROM AUTHOR]

Introduction and objectives: Sustainable development is defined as development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. It encompasses economic, social, and environmental dimensions that must be considered simultaneously. With the increasing importance of sustainable development, many companies worldwide are motivated, either proactively or reactively, to collect their used products. In such circumstances, establishing a reverse logistics network based on sustainable development is essential. The decision to outsource logistics has gained significance due to the need to avoid fixed costs, heavy investment, and achieve economic advantages, with many companies recognizing the potential benefits of high-quality logistics services. Method: This research presents a mixed integer programming model for planning reverse logistics outsourcing in the assembly cycle of the automotive industry, focusing on a cost-oriented objective function. The research scope includes the assembly cycle of production lines, specifically prioritizing high-volume car manufacturers (light vehicles), and focuses on the Saipa Automotive Industrial Group, including the Ryan Saipa Leasing Group. The research period spans from 1389 to 1398 in the Iranian calendar. Variables such as non-commercial receivables, total assets, operating profit, net profit, and market value were evaluated using MATLAB software based on published statistics from Saipa. Findings: The research findings indicate that among the variables of non-commercial receivables, total assets, operating profit, net profit, and market value, net profit to operating profit and sales (operating income) are of significant importance. The highest amount of non-commercial receivables for Saipa occurred in 1398 during the summer, while the highest total assets were recorded in 1392 during the summer. The highest operating profit was observed in 1398 during the winter, and the highest net profit was in 1390 during the spring. The degree of data convergence was calculated in the regression charts of sales (operating income) to operating profit and net profit to operating profit for the years 1389-1398. The degree of data convergence in the regression chart of sales to operating profit based on the conceptual model in 1398 was 0.9895, and for net profit to operating profit in 1398, it was 0.9961. The regression rate for the conceptual model in the test phase was 0.79, and in the overall processing stage, it was also 0.79. The histogram error rate was calculated for all three stages of learning, validation, and testing, with an error rate of 0.002375, which is acceptable due to its proximity to zero. Comparing these results with other studies shows an improvement in the regression and error rate in the analysis of the objective function. Conclusion: Based on the calculated weight of the criteria in two-way assembly line balancing issues, it can be concluded that the decision team pays special attention to strategic issues in addition to production issues. The production rate of the line, which is the inverse of the production cycle time, affects the company's market share in the long term and increases its market share. [ABSTRACT FROM AUTHOR]

Increasing the depth of microchannels on metallic bipolar plates (BPPs) in PEM fuel cells leads to an increase in the efficiency. In this research, the stamping process has been applied for manufacturing of the BPPs made of commercially pure titanium with a direct parallel flow field. The effect of process parameters including die clearance, forming speed, and sheet/die friction coefficient on the filling rate and thinning of the BPPs was investigated. The required tests were designed via the response surface method (RSM), implemented by a validated finite elements (FE) model, and the desired outputs were extracted. Then, a quadratic equation was presented for predicting the filling rate based on the input parameters using the regression method. In the following, using the artificial bee colony algorithm, the coefficients of the mentioned equation were enhanced and its error was decreased almost by 53%. Finally, an artificial neural network (ANN) was used to predict the filling rate. The results demonstrated that the proposed ANN model is very effective and approximates the filling rate of the microchannel with high accuracy. [ABSTRACT FROM AUTHOR]

Introduction and objectives: Sustainable development is defined as development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. It encompasses economic, social, and environmental dimensions that must be considered simultaneously. With the increasing importance of sustainable development, many companies worldwide are motivated, either proactively or reactively, to collect their used products. In such circumstances, establishing a reverse logistics network based on sustainable development is essential. The decision to outsource logistics has gained significance due to the need to avoid fixed costs, heavy investment, and achieve economic advantages, with many companies recognizing the potential benefits of high-quality logistics services.Method: This research presents a mixed integer programming model for planning reverse logistics outsourcing in the assembly cycle of the automotive industry, focusing on a cost-oriented objective function. The research scope includes the assembly cycle of production lines, specifically prioritizing high-volume car manufacturers (light vehicles), and focuses on the Saipa Automotive Industrial Group, including the Ryan Saipa Leasing Group. The research period spans from 1389 to 1398 in the Iranian calendar. Variables such as non-commercial receivables, total assets, operating profit, net profit, and market value were evaluated using MATLAB software based on published statistics from Saipa.Findings: The research findings indicate that among the variables of non-commercial receivables, total assets, operating profit, net profit, and market value, net profit to operating profit and sales (operating income) are of significant importance. The highest amount of non-commercial receivables for Saipa occurred in 1398 during the summer, while the highest total assets were recorded in 1392 during the summer. The highest operating profit was observed in 1398 during the winter, and the highest net profit was in 1390 during the spring. The degree of data convergence was calculated in the regression charts of sales (operating income) to operating profit and net profit to operating profit for the years 1389-1398. The degree of data convergence in the regression chart of sales to operating profit based on the conceptual model in 1398 was 0.9895, and for net profit to operating profit in 1398, it was 0.9961. The regression rate for the conceptual model in the test phase was 0.79, and in the overall processing stage, it was also 0.79. The histogram error rate was calculated for all three stages of learning, validation, and testing, with an error rate of 0.002375, which is acceptable due to its proximity to zero. Comparing these results with other studies shows an improvement in the regression and error rate in the analysis of the objective function.Conclusion: Based on the calculated weight of the criteria in two-way assembly line balancing issues, it can be concluded that the decision team pays special attention to strategic issues in addition to production issues. The production rate of the line, which is the inverse of the production cycle time, affects the company's market share in the long term and increases its market share.

IntroductionRiver flow forecasting has been one of the important challenges of water resources management in recent decades, so many researchers have proposed different methods to improve the performance of forecasting models. In the last decade, artificial intelligence methods have been most widely used in the simulation of various processes, including hydrological processes, due to their flexibility and high accuracy in modeling. However, the results of these methods have always been associated with uncertainty due to several factors such as structure, algorithm, input data, and the type of method chosen for data calibration. One of the methods that can somewhat solve this problem is the uncertainty analysis of the predictions made by these models. Materials and MethodsIn this study, the uncertainty of the results of artificial neural network (ANN) and support vector machine (SVM) models in predicting the monthly flow of the river has been evaluated. In this research, the time series of the monthly flow of the Ghezelozan River using the data of the Bianlu-Yasaul Stream gauging station in 39 years from 1976 to 2014 was used, where 75% and 25% of the data was used for training and testing the models, respectively. In these models, to estimate the monthly flow of the Ghezelozan River, six different input combinations including the flow of one, two, and three months before and the number of months of the flow were used. Then, the accuracy and performance of the models were compared using the coefficient of determination (R) and root mean square of errors (RMSE). Finally, the uncertainty of the results of ANN and SVM models in predicting the monthly flow of the river was evaluated by the Monte-Carlo method using d-factor and 95PPU values. Results and DiscussionThe evaluation of the performance of the ANN model shows that the best performance is related to the combination where the flow of the previous two months and the number of the month of the flow are the inputs of the model so that R and RMSE indexes were obtained as 0.757 and 9.45, respectively. In the SVM model for the monthly river flow series, the best performance is related to the combination where the flow of one, two, and three months ago and the number of months of the flow were the inputs of the model, and the R and RMSE indexes for this input pattern were 0.729 and 10.946, respectively. After studying the uncertainty of the models, the results showed that the ANN model has more uncertainty in the output values compared to the SVM model, and this is while the d-factor of the ANN model, both in the training and test phase, it was more than the SVM model. The comparison of the uncertainty analysis of the results of the ANN and SVM models showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in the output values. So the number of observation data placed in the 95% confidence range (95PPU) of the ANN model compared to the SVM model has increased significantly in both the training and testing phases. Also, the results showed that both models have more uncertainty in the months with a large volume of water, which can be due to the complexity of the process and the involvement of uncertain factors in these months, as well as the effect of factors that are not considered in the structure of predictive models.ConclusionThe results of ANN and SVM models in predicting the monthly flow of the Ghezelozan River showed that although the ANN model with R-value equal to 0.757 and RMSE value equal to 9.45 has a good performance compared to the SVM model with R-value equal to 0.729 and RMSE value equal to 10.946 in predicting the river flow, the results of this model are associated with high uncertainty. The comparison of the uncertainty analysis of the results of ANN and SVM models by Monte-Carlo method showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in predicting the monthly flow of the Ghezelozan River and it is better than ANN model. According to the results of this research, taking into account the fact that advanced artificial intelligence models also have uncertainty, it is necessary to apply these methods in the fields of risk management and future planning to obtain the best performance.

Introduction Due to global warming, accurately estimating evaporation has become a key challenge in water resource management, and due to the important role it plays in the withdrawal of water from human reach, it has always attracted the attention of researchers. Therefore, modeling and awareness of the value of evaporation as one of the hydrological variables is of great importance in agricultural research and soil and water conservation. Gorgan was chosen for the study due to its proximity to the Caspian Sea with a humid climate and a higher rate of evaporation than other cities. On the other hand, Shiraz has a hot and dry climate, is located in central Iran far from water resources such as the sea, and has a lower evaporation rate. Kish also has a warm and humid climate due to its proximity to the sea, with a lower evaporation rate than Shiraz but higher than Gorgan. Several meteorological variables affect the process of evaporation and transpiration, and due to the complexity of the evaporation parameter, a method with high accuracy should be used to determine them. Recently, artificial neural network methods have become very popular among researchers due to their common use and the ease with which they can solve complex problems. Therefore, many intelligent algorithms have been suggested to find the best solution for complex engineering problems, as they can find optimal answers faster and more accurately. Materials and Methods Artificial neural networks are designed based on inspiration from the memory and learning mechanisms in the human brain. To train artificial neural networks, a set of valid input and output data is used based on the type of problem. The accuracy of the network output depends on the amount of training data and how the inputs and their features are processed. To design different scenarios for adjusting input data, the correlation values of the data with evaporation were used. In this study, three synoptic stations with different climates, including Gorgan, Shiraz, and Kish, were chosen. Three stations with different climates were used to better evaluate and repeat the steps of the method so that the efficiency of the method could be more accurately assessed. Considering the importance of the value of evaporation in nature, evaporation modeling with the ANN and its combination with the COOT algorithm, which mimics the natural life of a COOT bird, was performed using five meteorological parameters, including the minimum air temperature, maximum air temperature, wind speed, average relative humidity, and sunshine hours on a monthly between 2000 and 2022. The dataset was divided into two phases: training (70 % of the dataset) and testing (30% of the dataset). To evaluate the performance of developed models, statistical indices of these models such as correlation coefficient (R), root-mean-square error (RMSE), Nash-Sutcliffe coefficient (NS), and their graphical representations were compared with each other. Results and Discussion As mentioned, four models of ANN-COOT with varying input parameters were developed and compared to four conventional ANN models. Statistical performances were calculated, and comparison plots were made in the training and testing phases to find the most adequate model for the prediction of evaporation. Comparing of obtained results from statistical indices for the testing phase revealed that the COOT-ANN4 model had the best performance for Gorgan with the R, RMSE, and NS equal to 0.99, 8.19, and 0.99 respectively. Shiraz also obtained values of the R, RMSE, and NS equal to 0.99, 18.43, and 0.98 respectively. Similarly, for Kish, the values of the R, RMSE, and NS equal to 0.97, 19.36, and 0.93 respectively, have better performance than the other models. Compared with the results of different input combinations, the hybrid ANN-COOT model (ANN-COOT4) at three stations was found superior with input combinations of Tmin, Tmax, SSH, RH, and WS. Additionally, to evaluate the accuracy of developed models, Scatter plots, Violin plots, Relative error percent plots (RE %), Taylor diagrams, and Histograms were drawn. By comparing the graphical representations, it can be determined that the hybrid COOT with ANN4, namely the COOT-ANN4 model, has improved the artificial neural network at Gorgan, Shiraz, and Kish stations. Conclusion The algorithm of COOT is an optimization algorithm that is generally used to solve optimization problems. As observed from the overall performance of the results of the hybrid model in predicting evaporation, the objective function was minimized. The results indicated that scenario four of the COOT-ANN4 hybrid model with input parameters of minimum temperature, maximum temperature, sunshine hours, relative humidity, and wind speed has better accuracy and performance at all three stations. In general, the findings of this study revealed that the COOT algorithm can improve the artificial neural network (ANN) structure in any climate and provide a hybrid model with higher accuracy and less error for modeling the evaporation parameter. Considering that the COOT algorithm is powerful and efficient, it is better to use it in various fields to improve the performance and accuracy of models. The testing results revealed that the lowest Root Mean Square Error RMSE (18.43, 19.36 and 8.19) and highest coefficient of correlation R (0.99, 0.97, and 0.99), and the highest Nash–Sutcliffe Efficiency Coefficient (N-S) (0.98, 0.93 and 0.99) attained by the ANN-COOT4 hybrid model (relative to other ANN and ANN-COOT models) tested for three selected stations in Shiraz, Kish and Gorgan sites. Concerning the predictive efficiency, the developed ANN-COOT hybrid model, improved the modeling performance at extreme points, which outperforms the ANN model, indicating its capability in the prediction of monthly evaporation.

IntroductionPredicting river flow is one of the most crucial aspects in water resources management. Improving forecasting methods can lead to a reduction in damages caused by hydrological phenomena. Studies indicate that artificial neural network models provide better predictions for river flow compared to physical and conceptual models. However, since these models may not offer reliable performance in estimating unstable data, using preprocessing techniques is necessary to enhance the accuracy and performance of artificial neural networks in estimating hydrological time series with nonlinear relationships. One of these methods is wavelet transformation, which utilizes signal processing techniques. Materials and MethodsIn this study, to evaluate the efficiency of discrete and continuous wavelet types in the Wavelet-Artificial Neural Network (WANN) hybrid model for monthly flow prediction, a case study was conducted on the Kardeh Dam watershed in the northeast of Iran, serving as a water source for part of Mashhad city and irrigation downstream agricultural lands. Monthly streamflow estimates for the upstream sub-basin of the Kardeh Dam were obtained from the meteorological and hydrometric stations' monthly statistics over a 30-year period (1991-2020). The WANN model is a hybrid time series model where the output of the wavelet transform serves as a data preprocessing method entering an artificial neural network as the predictive model. The combination of wavelet analysis and artificial neural network implies using wavelet capabilities for feature extraction, followed by the neural network to learn patterns and predict data, potentially enhancing the models' performance by leveraging both methods. The 4-fold cross-validation method was employed for the artificial neural network model validation, where the model underwent validation and accuracy assessment four times, each time using 75% of the data for training and the remaining 25% for model validation. The final results were presented by averaging the validation and accuracy results obtained from each of the four model runs. To evaluate and compare the performance of the models used in this study, three evaluation indices, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (R), were employed. Results and DiscussionThe analysis of meteorological and hydrometric data in this study revealed that monthly streamflow in two time steps, T-1 and T-2, were the most effective predictive variables. Each of the two runoff variables of the previous month (Qt-1) and the previous two months (Qt-2) were analyzed by each of the Haar and Fejer-Korovkin2 discrete wavelet transforms and the two continuous Symlet3 and Daubechies2 wavelets at three levels. The results of each level of decomposition was given as input to the ANN model. The presented results at each decomposition level indicated that hybrid models could accurately predict lower flows compared to the single ANN model, and the estimation of maximum values also significantly improved in the hybrid models. Among the wavelets used, Haar wavelets exhibited the weakest performance, and the less commonly employed Kf2 wavelet showed a moderate performance. Since the Haar and Fk2 wavelets, with their discrete structure, did not perform well in decomposing continuous monthly streamflow data, continuous wavelet models outperformed discrete wavelet models. The hybrid models, combining wavelet analysis and artificial neural networks, demonstrated up to an 11% improvement over the performance of the single neural network model. ConclusionStreamflow is a crucial element in the hydrological cycle, and predicting it is vital for purposes such as flood prediction and providing water for consumption. The objective of this research was to evaluate the performance of different types of discrete and continuous wavelet models at various decomposition levels in enhancing the efficiency of artificial neural network (ANN) models for streamflow prediction. Since climate and watershed characteristics can influence the nature of data fluctuations and, consequently, the results of the wavelet model decomposition, choosing an appropriate wavelet model is essential for obtaining the best results. Considering the existing variations in the results of different studies regarding the selection of the best wavelet type, it is suggested to use both continuous and discrete wavelet types in modeling to achieve the best predictions and select the optimal results. Given that a lower number of input variables in neural network models lead to higher accuracy in modeling results, it is recommended to perform decomposition at a two-level depth to reduce input components to the neural network model, thereby reducing the model execution time.

ABSTRACT: ABSTRACT: :Fiscal decentralization refers to the transfer of responsibility and authority of revenue and spending decisions from the central government to lower levels. In recent years, with the increase in energy consumption, global warming, carbon emissions, and climate change, the issue of fiscal decentralization and its environmental effects has received attention. Because fiscal decentralization has an important effect on promoting clean energy sources, reducing energy consumption and reducing carbon emissions. In the present study, Hybrid intelligent models have been used to developed to predict and analyze the effect of fiscal decentralization from two dimensions of expenditure and revenue on energy consumption and its environmental environment by combining particle swarm optimization algorithms, genetics and neural network. Controlled optimization algorithms enhance all prediction steps. For this purpose, seasonal data was used during the period 1996-2022. According to the research results, resilient backpropagation neural network in predicting revenue decentralization and Scaled conjugate gradient backpropagation neural network has a high power in predicting expenditure decentralization in the country. Also, the value of R statistic, which indicates the goodness of fit of the model, has the highest value for the revenue decentralization variable compared to the expenditure decentralization, and this indicates the better performance of the revenue decentralization model than the expenditure decentralization. The results of the combination optimizer model (GPA) and the neural simulation model (GAPSO) in predicting the decentralization of expenditure and revenue showed that the decentralization of revenue had the best performance compared to the model of decentralization of expenditure. a

Introduction River flow forecasting has been one of the important challenges of water resources management in recent decades, so many researchers have proposed different methods to improve the performance of forecasting models. In the last decade, artificial intelligence methods have been most widely used in the simulation of various processes, including hydrological processes, due to their flexibility and high accuracy in modeling. However, the results of these methods have always been associated with uncertainty due to several factors such as structure, algorithm, input data, and the type of method chosen for data calibration. One of the methods that can somewhat solve this problem is the uncertainty analysis of the predictions made by these models. Materials and Methods In this study, the uncertainty of the results of artificial neural network (ANN) and support vector machine (SVM) models in predicting the monthly flow of the river has been evaluated. In this research, the time series of the monthly flow of the Ghezelozan River using the data of the Bianlu-Yasaul Stream gauging station in 39 years from 1976 to 2014 was used, where 75% and 25% of the data was used for training and testing the models, respectively. In these models, to estimate the monthly flow of the Ghezelozan River, six different input combinations including the flow of one, two, and three months before and the number of months of the flow were used. Then, the accuracy and performance of the models were compared using the coefficient of determination (R) and root mean square of errors (RMSE). Finally, the uncertainty of the results of ANN and SVM models in predicting the monthly flow of the river was evaluated by the Monte-Carlo method using d-factor and 95PPU values. Results and Discussion The evaluation of the performance of the ANN model shows that the best performance is related to the combination where the flow of the previous two months and the number of the month of the flow are the inputs of the model so that R and RMSE indexes were obtained as 0.757 and 9.45, respectively. In the SVM model for the monthly river flow series, the best performance is related to the combination where the flow of one, two, and three months ago and the number of months of the flow were the inputs of the model, and the R and RMSE indexes for this input pattern were 0.729 and 10.946, respectively. After studying the uncertainty of the models, the results showed that the ANN model has more uncertainty in the output values compared to the SVM model, and this is while the d-factor of the ANN model, both in the training and test phase, it was more than the SVM model. The comparison of the uncertainty analysis of the results of the ANN and SVM models showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in the output values. So the number of observation data placed in the 95% confidence range (95PPU) of the ANN model compared to the SVM model has increased significantly in both the training and testing phases. Also, the results showed that both models have more uncertainty in the months with a large volume of water, which can be due to the complexity of the process and the involvement of uncertain factors in these months, as well as the effect of factors that are not considered in the structure of predictive models. Conclusion The results of ANN and SVM models in predicting the monthly flow of the Ghezelozan River showed that although the ANN model with R-value equal to 0.757 and RMSE value equal to 9.45 has a good performance compared to the SVM model with R-value equal to 0.729 and RMSE value equal to 10.946 in predicting the river flow, the results of this model are associated with high uncertainty. The comparison of the uncertainty analysis of the results of ANN and SVM models by Monte-Carlo method showed that the SVM model with d-factor and 95PPU values equal to 0.155 and 17.241, respectively, compared to the ANN model with d-factor and 95PPU values equal to 0.993 and 85.470, respectively, has less uncertainty in predicting the monthly flow of the Ghezelozan River and it is better than ANN model. According to the results of this research, taking into account the fact that advanced artificial intelligence models also have uncertainty, it is necessary to apply these methods in the fields of risk management and future planning to obtain the best performance. [ABSTRACT FROM AUTHOR]

Introduction Due to global warming, accurately estimating evaporation has become a key challenge in water resource management, and due to the important role it plays in the withdrawal of water from human reach, it has always attracted the attention of researchers. Therefore, modeling and awareness of the value of evaporation as one of the hydrological variables is of great importance in agricultural research and soil and water conservation. Gorgan was chosen for the study due to its proximity to the Caspian Sea with a humid climate and a higher rate of evaporation than other cities. On the other hand, Shiraz has a hot and dry climate, is located in central Iran far from water resources such as the sea, and has a lower evaporation rate. Kish also has a warm and humid climate due to its proximity to the sea, with a lower evaporation rate than Shiraz but higher than Gorgan. Several meteorological variables affect the process of evaporation and transpiration, and due to the complexity of the evaporation parameter, a method with high accuracy should be used to determine them. Recently, artificial neural network methods have become very popular among researchers due to their common use and the ease with which they can solve complex problems. Therefore, many intelligent algorithms have been suggested to find the best solution for complex engineering problems, as they can find optimal answers faster and more accurately. Materials and Methods Artificial neural networks are designed based on inspiration from the memory and learning mechanisms in the human brain. To train artificial neural networks, a set of valid input and output data is used based on the type of problem. The accuracy of the network output depends on the amount of training data and how the inputs and their features are processed. To design different scenarios for adjusting input data, the correlation values of the data with evaporation were used. In this study, three synoptic stations with different climates, including Gorgan, Shiraz, and Kish, were chosen. Three stations with different climates were used to better evaluate and repeat the steps of the method so that the efficiency of the method could be more accurately assessed. Considering the importance of the value of evaporation in nature, evaporation modeling with the ANN and its combination with the COOT algorithm, which mimics the natural life of a COOT bird, was performed using five meteorological parameters, including the minimum air temperature, maximum air temperature, wind speed, average relative humidity, and sunshine hours on a monthly between 2000 and 2022. The dataset was divided into two phases: training (70 % of the dataset) and testing (30% of the dataset). To evaluate the performance of developed models, statistical indices of these models such as correlation coefficient (R), root-mean-square error (RMSE), Nash-Sutcliffe coefficient (NS), and their graphical representations were compared with each other. Results and Discussion As mentioned, four models of ANN-COOT with varying input parameters were developed and compared to four conventional ANN models. Statistical performances were calculated, and comparison plots were made in the training and testing phases to find the most adequate model for the prediction of evaporation. Comparing of obtained results from statistical indices for the testing phase revealed that the COOT-ANN4 model had the best performance for Gorgan with the R, RMSE, and NS equal to 0.99, 8.19, and 0.99 respectively. Shiraz also obtained values of the R, RMSE, and NS equal to 0.99, 18.43, and 0.98 respectively. Similarly, for Kish, the values of the R, RMSE, and NS equal to 0.97, 19.36, and 0.93 respectively, have better performance than the other models. Compared with the results of different input combinations, the hybrid ANN-COOT model (ANN-COOT4) at three stations was found superior with input combinations of Tmin, Tmax, SSH, RH, and WS. Additionally, to evaluate the accuracy of developed models, Scatter plots, Violin plots, Relative error percent plots (RE %), Taylor diagrams, and Histograms were drawn. By comparing the graphical representations, it can be determined that the hybrid COOT with ANN4, namely the COOT-ANN4 model, has improved the artificial neural network at Gorgan, Shiraz, and Kish stations. Conclusion The algorithm of COOT is an optimization algorithm that is generally used to solve optimization problems. As observed from the overall performance of the results of the hybrid model in predicting evaporation, the objective function was minimized. The results indicated that scenario four of the COOT-ANN4 hybrid model with input parameters of minimum temperature, maximum temperature, sunshine hours, relative humidity, and wind speed has better accuracy and performance at all three stations. In general, the findings of this study revealed that the COOT algorithm can improve the artificial neural network (ANN) structure in any climate and provide a hybrid model with higher accuracy and less error for modeling the evaporation parameter. Considering that the COOT algorithm is powerful and efficient, it is better to use it in various fields to improve the performance and accuracy of models. The testing results revealed that the lowest Root Mean Square Error RMSE (18.43, 19.36 and 8.19) and highest coefficient of correlation R (0.99, 0.97, and 0.99), and the highest Nash–Sutcliffe Efficiency Coefficient (N-S) (0.98, 0.93 and 0.99) attained by the ANN-COOT4 hybrid model (relative to other ANN and ANN-COOT models) tested for three selected stations in Shiraz, Kish and Gorgan sites. Concerning the predictive efficiency, the developed ANN-COOT hybrid model, improved the modeling performance at extreme points, which outperforms the ANN model, indicating its capability in the prediction of monthly evaporation. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of measuring soil cation exchange capacity (CEC), a vital factor influencing soil fertility, by exploring the impact of grouping soil samples based on different characteristics on the performance of estimation models. Recognizing the difficulties associated with traditional CEC measurement methods, the study employs a cost-effective and rapid approach using various models and equations. The research, conducted at Bu Ali Sina University in Hamedan, utilizes a substantial dataset of 45,948 soil samples from the standardized database of world soils. Soil samples are initially categorized into different groups, and nine estimator variables are examined across 11 models for the entire dataset and specific classes within each group. These variables include soil texture components, organic carbon, calcium sulfate, calcium carbonate, bulk density, base saturation percentage, total exchangeable base cations, and soil reaction. The results demonstrate that grouping soil samples, especially based on texture classes, significantly improves the performance of artificial neural network models, with a remarkable 87% relative improvement coefficient in the test section. The study reveals that data grouping enhances the model's estimation capabilities, as evidenced by reduced root mean square error (RMSE) values in the test sections for different texture classes. In conclusion, the findings suggest that utilizing functions derived from grouped data offers an effective and cost-efficient method for estimating soil cation exchange capacity. This approach provides valuable insights for soil fertility management, offering a simplified yet accurate means of assessing this critical soil parameter. [ABSTRACT FROM AUTHOR]

IntroductionPredicting river flow is one of the most crucial aspects in water resources management. Improving forecasting methods can lead to a reduction in damages caused by hydrological phenomena. Studies indicate that artificial neural network models provide better predictions for river flow compared to physical and conceptual models. However, since these models may not offer reliable faformance in estimating unstable data, using preprocessing techniques is necessary to enhance the accuracy and faformance of artificial neural networks in estimating hydrological time series with nonlinear relationships. One of these methods is wavelet transformation, which utilizes signal processing techniques. Materials and MethodsIn this study, to evaluate the efficiency of discrete and continuous wavelet types in the Wavelet-Artificial Neural Network (WANN) hybrid model for monthly flow prediction, a case study was conducted on the Kardeh Dam watershed in the northeast of Iran, serving as a water source for part of Mashhad city and irrigation downstream agricultural lands. Monthly streamflow estimates for the upstream sub-basin of the Kardeh Dam were obtained from the meteorological and hydrometric stations' monthly statistics over a 30-year faiod (1991-2020). The WANN model is a hybrid time series model where the output of the wavelet transform serves as a data preprocessing method entering an artificial neural network as the predictive model. The combination of wavelet analysis and artificial neural network implies using wavelet capabilities for feature extraction, followed by the neural network to learn patterns and predict data, potentially enhancing the models' faformance by leveraging both methods. The 4-fold cross-validation method was employed for the artificial neural network model validation, where the model underwent validation and accuracy assessment four times, each time using 75% of the data for training and the remaining 25% for model validation. The final results were presented by averaging the validation and accuracy results obtained from each of the four model runs. To evaluate and compare the faformance of the models used in this study, three evaluation indices, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (R), were employed. Results and DiscussionThe analysis of meteorological and hydrometric data in this study revealed that monthly streamflow in two time steps, T-1 and T-2, were the most effective predictive variables. Each of the two runoff variables of the previous month (Qt-1) and the previous two months (Qt-2) were analyzed by each of the Haar and Fejer-Korovkin2 discrete wavelet transforms and the two continuous Symlet3 and Daubechies2 wavelets at three levels. The results of each level of decomposition was given as input to the ANN model. The presented results at each decomposition level indicated that hybrid models could accurately predict lower flows compared to the single ANN model, and the estimation of maximum values also significantly improved in the hybrid models. Among the wavelets used, Haar wavelets exhibited the weakest faformance, and the less commonly employed Kf2 wavelet showed a moderate faformance. Since the Haar and Fk2 wavelets, with their discrete structure, did not faform well in decomposing continuous monthly streamflow data, continuous wavelet models outfaformed discrete wavelet models. The hybrid models, combining wavelet analysis and artificial neural networks, demonstrated up to an 11% improvement over the faformance of the single neural network model. ConclusionStreamflow is a crucial element in the hydrological cycle, and predicting it is vital for purposes such as flood prediction and providing water for consumption. The objective of this research was to evaluate the faformance of different types of discrete and continuous wavelet models at various decomposition levels in enhancing the efficiency of artificial neural network (ANN) models for streamflow prediction. Since climate and watershed characteristics can influence the nature of data fluctuations and, consequently, the results of the wavelet model decomposition, choosing an appropriate wavelet model is essential for obtaining the best results. Considering the existing variations in the results of different studies regarding the selection of the best wavelet type, it is suggested to use both continuous and discrete wavelet types in modeling to achieve the best predictions and select the optimal results. Given that a lower number of input variables in neural network models lead to higher accuracy in modeling results, it is recommended to faform decomposition at a two-level depth to reduce input components to the neural network model, thereby reducing the model execution time. [ABSTRACT FROM AUTHOR]

Dez dam has an essential role in controlling the floods and water supply in the plains of Khouzestan. Therefore, accurate and reliable prediction of the flow input to the reservoir is crucial to make decisions about the exploitation and management of this reservoir. In this research, a novel WAVELET-ARIMA-NARX model was developed to predict the monthly inflow to Dez dam reservoir. The WAVELET method was used to break down the time series into sub-series and better analyzing, the ARIMA model was used to model the linear component of the analyzed series, and the NARX model was used to model the error resulting from the WAVELET-ARIMA model. The NDVI index was also used to measure the accuracy change of the WAVELET-ARIMA-NARX hybrid model. The results showed that the prediction performance of the WAVELET-ARIMA-NARX hybrid model has improved significantly compared to the ARIMA model. In such a way according to the RMSE criterion, the prediction accuracy in the verification and training stages has decreased by 74% and 82%, compared to the ARIMA and WAVELET-ARIMA model, respectively. Also, the NDVI parameter along with the average temperature and rainfall of the basin as an input of the NARX model has increased the accuracy of the model. In the model with 10 neurons in the hidden layer, compared to the two-parameter model of rainfall and NDVI with 15 neurons, in evaluation section, the MAE values have decreased from 27.2 to 18.5 and the RMSE from 0.45 to 0.26. These values indicate the importance and impact of simultaneous consideration of three parameters on forecasting accuracy [ABSTRACT FROM AUTHOR]

Objective: Predicting the results of the current quality improvement activities is one of the concerns of the officers of the armed forces officer universities. Taking advantage of the capabilities of the artificial neural network and taking into account the aforementioned concern, the present research has presented a model to predict the process of the results of the effective factors in the military educational activities of one of the officers' universities. Methodology: The statistical population is all the young officers under training in the combat camp in Kohestan University under study (from 2018 to 2022). In the first step, the data related to the factors and parameters of the training-educational activities of the military camp in the mountains were collected for the mentioned five-year time period. In the next step, pre-processing of the data was done and using the proposed IRNN algorithm and its coding in Python, the appropriate model was built and its validation was done. Findings: By using the built model and the available data, it was predicted the quality of the training performance of the combat camp. Originality: The results showed that with the continuation of the current policies, all the factors will have an upward trend except for the factor of "updating the level of knowledge and skills of the teaching staff". Therefore, proper planning should be done in order to update the level of knowledge and skills of the educational staff of specialized educational activities

IntroductionWater is the source of life and a strategic resource for human societies. The need for this vital resource is increasing exponentially due to the increase in population and the development of industry and agriculture. People are forced to use underground water because surface water is not generally and permanently responsive to diverse needs. A decrease in their volume and many problems have been caused by the excessive use of these resources. This crisis has caused regional crises caused by the imbalance of resources and consumption, along with climate changes, has raised the issue of integrated management of water resources more than ever. Agricultural land has been developed due to the increase in population and the need for more food. Programs without principles that rely solely on the quality and quantity of underground water resources have been harmful. Groundwater aquifers are transformed into sources of the country's needs due to the heterogeneous and untimely temporal and spatial distribution of discharges and surface water flows. In recent years, with the increase in water demand and the non-supply of a significant part of it by surface water sources, the extraction - permitted and unauthorized - of underground water sources has been given much attention; so that the level of underground aquifers has decreased dramatically across the country. The purpose of the present study was to investigate the impact of the important variables of precipitation, inflation and annual population as a representative of climatic, economic and social factors on the fluctuations of the underground water level in Urmia region. Material and MethodsIn the present study, the impact of three factors, precipitation, population and inflation, on the subsidence of the Urmia Plain aquifer has been investigated. To do this, multiple linear regressions was performed between the data of the annual loss of the groundwater level during 38 years, 1981 to 2019 with three variables of precipitation, population and inflation index of the previous year. According to the previous researches, firstly, an index of inflation has been established by comparing the average loss of the piezometric level of the underground water in Urmia region as a dependent variable, with the three independent variables of the average rainfall of water year as the most important climatic factor, the annual population of the major centers of human concentration located in the Urmia plain of previous year, and the base coefficient of the annual monetary value of previous year compared to 1981 using a multivariable linear regression. Then, the outcome is compared to the outcomes of artificial neural networks such as four-layer perceptron, three-layer perceptron, and radial basis function. All three networks have an input layer with three neurons to receive the values of the three independent variables of precipitation, population and inflation. One or two hidden layers with a number of neurons, to perform calculations and process the relationship between independent and dependent variables; and an output layer with a neuron to provide the processing results i.e., the estimated aquifer subsidence rate. The data used in the present study were derived from the years 1981-2019. The reference of the aquifer level data is the hydrograph extracted from 67 piezometer wells in the area by the underground water unit of basic studies of the West Azerbaijan Regional Water Company. The annual rainfall data reference is of the Urmia camp evaporation station located in the company premises, which is well controlled and highly reliable as an indicator of rainfall changes in the region. Population data is sourced from the Iranian Statistics Center, while inflation data is sourced from the Central Bank of Iran. Results and DiscussionAccording to the results of the reviewed models, despite the differences in the values of the numerical results, in all four models: multivariate linear regression, perceptron artificial neural networks of the four layers MLP:3-2-2-1, and the three layers MLP:3-5-1 and the radial basis function RBF: 3-5-1, it can be seen that the importance of the independent variables under study are population, inflation and annual precipitation respectively. It is obvious that a larger population needs more food, clothing, housing, etc., which, according to the concept of virtual water, ultimately leads to more use of the limited available water and soil resources. Economic activity, particularly agriculture, is increased due to the depreciation of currency and decrease in people's purchasing power, which is a result of the decrease in purchasing power and the depreciation of currency. This problem has also led to the change of land use of natural resources to agricultural lands that are either rainfed or irrigated. Explaining that rain fed lands cause more rainwater loss through capture and then evaporation and transpiration by plants planted by farmers. Irrigation of agricultural plants or gardens of irrigated lands - mainly with unauthorized water harvesting - ultimately leads to more water consumption. Additionally, humans have exploited underground water resources due to the inappropriate and untimely distribution of rainfall and surface water resources. Although by adopting new management methods, both social and economic, and improving water productivity, despite the increase in demand for water, despite our efforts to protect this vital, sensitive, and strategic resource, statistical studies, including the current results, demonstrate that we have not chosen the correct solutions. Considering some irreparable effects of the aquifer level drop, including irreversible changes in the mechanical characteristics of the soil, which lead to more vulnerability of infrastructures and facilities; the emphasis is placed on comprehensive water resource management and the concept of virtual water and its trade.

AbstractIntroduction: An artificial neural network (ANN) is a powerful data-driven tool capable of learning the linear and nonlinear relationships governing different systems. However, determining the best-performing algorithm in terms of convergence speed and accuracy for a particular problem is still a fundamental challenge for users of artificial neural networks.Methods: We investigated the most effective algorithm among widely used processes to simulate and estimate nonlinear water quality parameters. For this purpose, we constructed 42 models combining artificial neural network topology (single or multilayer) and training processes. The quality parameters’ data acquired at 107 wells throughout the aquifer of Qorveh-Dehgolan plain were used for training (data from 1996 to 2013) and to test (data from 2014 to 2016) each model.Findings: The results showed that artificial neural networks with a hidden layer that benefits from the optimal number of neurons could simulate the aquifer behavior with high accuracy and in less time. Also, increasing the number of hidden layers while increasing the response accuracy increases the number of optimal network neurons and the duration of the problem analysis. Finally, artificial neural networks based on the Broyden-Fletcher-Goldfarb (BFG) method had the highest efficiency in simulating aquifer behavior, although the performance of the Levenberg Marquart (LM) method is very close to BFG. BFG is more efficient than LM due to its lower Mean Square Error and Standard Deviation (3.46 and 3.09, respectively).

Introduction: Landscape in protected areas is an advantage of tourism in nature, and as a result, aesthetic assessment and landscape quality assessment are necessary in these areas. This is especially important in the mentioned areas where tourism is primarily eco-tourism and based on visits. Therefore, this study was done with the aim of modeling landscape visual aesthetic quality assessment with the purpose of tourism in protected areas using artificial neural network to predict the aesthetic value of the landscape and prioritize the influential variables of the model. Material and Methods: The current research was carried out in the Central Alborz protected area under the management of Alborz Province. In this study, in order to assess the landscape visual aesthetic quality of protected areas with the aim of tourism, a combination of comprehensive assessment perspective and the artificial neural network modeling method was used; a comprehensive assessment perspective includes the user perspective by completing the questionnaire and using 19 objective variables including 15 landscape objective criteria (diversity of natural and human covers, vegetation form diversity, trees composition form, water body form, color diversity, water landscape, rock landscape, roads and trails landscape, tree and shrub vegetation landscape, grassy and bushy vegetation landscape, bare and uncovered surfaces landscape, buildings and structures landscape, sky landscape, hard surface ratio, and soft surface ratio) and 4 criteria related to viewpoints characteristics. For this purpose, first, homogeneous landscape assessment units were characterized using six spatial indicators (elevation, slope (%), vegetation landscape, village visibility, and permanent and seasonal river visibilities). Then, 100 photos of the scenery of the area were taken and modeling was done using the multilayer perceptron network method. In the next step, the sensitivity analysis of the model for assessing the aesthetic quality of the landscape was done in order to prioritize and determine the most effective visual aesthetic criteria in the assessment of the visual aesthetic quality of the landscapes in the region. Finally, the decision support system for assessing the landscape visual aesthetic quality in protected areas for the purpose of tourism was designed. Results and Discussion: The model with the structure of 19-6-1 (19 input variables, 6 neurons in the hidden layer, and one output variable) with Log-Sigmoid transfer functions in the hidden layer and linear in the output layer and Levenberg-Marquardt optimization algorithm, with explanatory coefficients in the three data sets, namely training, validation and test equal to 0.7, 0.75 and 0.70 were introduced as the optimal structure of the model for assessing the landscapes visual aesthetic quality of protected areas with the purpose of tourism. According to the sensitivity analysis results, the parameters of the water landscape, the composition of trees, and the vegetation coverage with the sensitivity coefficients of 0.223, 0.147, and 0.104, respectively, showed the most significant impact on the landscapes's visual aesthetic quality in protected areas. Conclusion: The sensitivity analysis and identification of the most significant variables and criteria on the visual aesthetic quality of the landscapes of protected areas with the purpose of tourism showed that in order to achieve a high landscape visual aesthetic value and determine the intensive and extensive recreational zones in the protected areas, the water landscape should take the first priority of planning. The model presented in this research is a decision support system for assessing the landscape's visual aesthetic quality in protected areas for tourism and provides the possibility of predicting the visual aesthetic quality of landscapes in protected areas with similar ecological conditions and ecosystems. Also, the model presented in this study can be used in preparing the management plan and zoning of protected areas, especially in recreational zones. [ABSTRACT FROM AUTHOR]

In order to realize the real-time interaction between virtual space and real space, the digital twin system of nuclear reactor was built according to the actual nuclear reactor, with a large number of sensor devices and a large amount of data, resulting in problems of large storage space demand, low transmission efficiency and high complexity of data analysis. To solve the above problems, it is necessary to optimize the sensor arrangement and achieve efficient data storage, transmission and analysis. Few studies have been made to find feature parameters while obtaining their physical meaning and maintaining high efficiency simultaneously, which is the purpose of this paper. In this study, a parameter inversion model was built for the digital twin system of space thermionic reactors by combining clustering algorithm and artificial neural network based on random search optimization, so as to realize the inversion of core temperature data under heat pipe failure conditions. The main process of the model can be grouped into two parts, feature parameter extraction and parameter inversion. In the part of feature extraction, data were reduced from high dimension to low dimension, which constituted a data reduction. The temperature data were extracted from four areas of the space thermionic reactors core from the inside out, which were fuel, emitter, receiving pole and coolant, under 20% heat pipe failure condition. Temperature parameters can be regarded as numerical vectors with the same dimension as the number of time nodes, so the clustering method can divide these temperature parameters into different categories, and the parameter closest to the center of each category was taken as the parameter with typical change characteristics. Silhouette Score is usually used for evaluating the effect of clustering, which can be used to find the proper number of clusters. Therefore, K-means clustering algorithm and Silhouette Score were used as evaluation indicators to select the feature parameters of different regions of the core. In the second part, the feature parameters were inverted to other parameters by a fully connected artificial neural network, its hyper parameters optimized by random search configuration. The optimizing process was executed by setting a range for different hyper parameters like the number of hidden layers, the nodes of each layer and the learning rate, and then choosing a combination that had the smallest loss. Then the feature parameters were used as input and inverted to the rest as output. The inversion model was then verified with the temperature data of 15% heat pipe failure condition. The results show that by using the parameter inversion of fuel, emitter, receiving pole and coolant, the relative error of temperature inversion value is 0.55%, 0.41%, 0.19% and 0.18% respectively, and the feature parameters used for inversion are no more than 8% of the total parameters. Therefore, the parameter inversion model established in this study can obtain the physical meaning of the feature parameters and reverse the space reactor core temperature parameters with high accuracy, therefore resisting the influence of probe damage and data loss. The application of this method in the digital twin system of nuclear reactor can also provide an evaluation basis for the future installation of detectors in small nuclear reactors. [ABSTRACT FROM AUTHOR]

In this research, in order to select an appropriate model for predicting river flow in the Bashar River basin, data-driven models including multilayer perceptron artificial neural network and fuzzy neural network from the Sugeno fuzzy inference system were used using the clustering reduction method, and the analysis of uncertainty of these models was investigated. The data used in this research includes monthly values of rainfall and average temperature at rain gauge stations, as well as monthly average river discharge at the hydrological station located in the Bashar River basin from the years 1979-1980 to 2018-2019. The sensitivity analysis results on the number of neurons in the hidden layer of the neural network showed that the optimal number of neurons in the hidden layer for the input combination is 13.Based on the root mean square error (RMSE) index, the best combination of input variables for simulating river flow in both the neural network and neural-fuzzy network models was determined to be the input combination consisting of average river discharge with one-month and two-month lag along with monthly rainfall values and monthly rainfall values with one-month and two-month lag. In order to investigate the uncertainty of the models, the artificial neural network and neural-fuzzy network models were employed in the form of Monte Carlo sampling. The results of the uncertainty analysis showed that, for the same random input variables, the deviation from the mean in the output of the neural network model is higher than that of the neuralfuzzy network model. Additionally, the results obtained from calculating the confidence interval indicate that the confidence interval for different confidence levels is smaller in the neural-fuzzy network compared to the neural network. For example, in the neural network model with 98% confidence, the output is within the range of (0.64 and 0.36), whereas in the neural-fuzzy network model with 98% confidence, the output is between the range of (0.69 and 0.53). This indicates a higher level of uncertainty in the results of the neural network model. [ABSTRACT FROM AUTHOR]

Fiscal decentralization refers to the transfer of responsibility and authority of revenue and spending decisions from the central government to lower levels. In recent years, with the increase in energy consumption, global warming, carbon emissions, and climate change, the issue of fiscal decentralization and its environmental effects has received attention. Because fiscal decentralization has an important effect on promoting clean energy sources, reducing energy consumption and reducing carbon emissions. In the present study, Hybrid intelligent models have been used to developed to predict and analyze the effect of fiscal decentralization from two dimensions of expenditure and revenue on energy consumption and its environmental environment by combining particle swarm optimization algorithms, genetics and neural network. Controlled optimization algorithms enhance all prediction steps. For this purpose, seasonal data was used during the period 1996-2022. In comparing the results of the implementation of each model, income decentralization has had better overall performance compared to the expenditure decentralization model due to the lowest values of root mean error, absolute mean error and the R highest value. According to the research results, resilient backpropagation neural network in predicting revenue decentralization and Scaled conjugate gradient backpropagation neural network has a high power in predicting expenditure decentralization in the country. Also, the value of R statistic and, the results of the combination optimizer model (GPA) and the neural simulation model (GAPSO) in predicting the decentralization of expenditure and revenue showed that the decentralization of revenue had the best performance compared to the model of decentralization of expenditure. [ABSTRACT FROM AUTHOR]

Analyzing and examining the price trend of an asset is a fundamental step in managing investment risk on that asset. Therefore, in markets, predicting the price trend of an asset is of special interest to traders and even plays a crucial role in a country's monetary policies. Based on this, in this paper, we will try to use the concept of fractal interpolation to predict the price trend of gold, given its price fluctuations and greater importance compared to other metals in markets. By analyzing the gold’s price trend using time series data with a fractal structure, we aim to determine the pattern of price trend to predict the price trend of gold ounces. Such an approach can provide the necessary tool to help investment decision-making in different time periods (short-term, medium-term, and possibly long-term). To achieve this, we first identify the presence of long-term memory in gold's price trend using the Hurst exponent. After confirming stability, we generate fractal data by calling the fractal interpolation algorithm and then predict the behavior of the corresponding time series data using a neural network algorithm based on fractal data. Finally, we compare the results obtained from calling the algorithms present in the literature on gold data. 1- IntroductionThe financial market data is unstable and irregular, and it sometimes contains missing data. To address these issues, researchers have developed different approaches, such as the fractal interpolation method. The aim of this study is to investigate the time series related to the gold market and determine whether it exhibits fractal characteristics. To generate fractal data, we can use the improved fractal interpolation algorithm (IFI). Then, we can use the support vector regression (SVR) algorithm, which is a type of machine learning method (SVM), to predict the price trend of gold in a specific time period. The price of an asset is directly proportional to its risk or fluctuation. Therefore, in the first phase, business owners and investors can determine the appropriate fee rate by analyzing the time series of data with fractal structure, and in the second phase, in the asset management phase, they can control losses caused by big fluctuations in returns and investment. The fluctuation of asset prices is a significant topic that has been studied by many researchers. They use linear or non-linear methods to predict and make appropriate use of these price fluctuations. In economic and social fields, fractal interpolation is often used to fit missing data and predict short-term trends due to the abundance of unstable and irregular data. In time series, data are collected at regular intervals according to a certain rule, and by analyzing the obtained data and with the help of different methods, the behavior of the series in the future can be approximately predicted. There are methods to determine whether a system is fractal or not, and thus, to calculate its fractal dimension. The method of calculating the dimension of the system depends on the type of its fractal structure. By determining the fractal dimension, the stability of the system can be investigated. Stability is a key factor in time series analysis, and the Hurst exponent is one of the criteria used to assess stability. Therefore, the fractal interpolation method can be implemented when the time series is stable. Hurst's exponent is a measure that identifies the long-term memory in time series. The R/S analysis criterion is one of the methods used to calculate the Hurst exponent. This criterion was first proposed by Hurst in his studies of natural phenomena such as the hydrological characteristics of the Nile Basin in 1951. In financial markets, the R/S analysis criterion is used to distinguish fractal from non-fractal systems, to identify the stability of trends, and to determine the length of life-time cycles. The range value of R in this index is equal to the difference between the lowest and highest deviation values from the cumulative average of the time series. Hurst normalized the value of the R range by using the standard deviation of the time series, relative to the fluctuations of the inputs of different time series, and defined the analysis criterion in a certain period of time. In this study, we aim to evaluate the performance of the fractal interpolation algorithm in predicting the trend of gold price based on time series data. The global ounce of gold is of great importance in world markets and experiences fluctuations, making it an ideal candidate for this analysis. To achieve this, we first calculate the Hurst exponent to determine the long-term memory of the gold price trend. We then generate fractal data using the fractal interpolation algorithm and apply the support vector regression (SVR) algorithm to predict the gold price trend. We compare the performance of Wang's algorithm and the Fracsion algorithm to determine the best method for predicting the gold price trend. The primary objective of this research is to examine the predictability of the price trend of gold and determine its price pattern. We analyze and evaluate this process by comparing it with past-oriented methods such as Wang's method. 2- Main ResultsWe have analyzed the results of the Wang algorithm and the Fracsion algorithm separately for their ability to predict the final price of gold in 2020, 2021, and 2022 using well-known evaluation criteria.Two algorithms, the Wong and Fracsion methods, are presented below for the purpose of numerical analysis.Wang, et al., in 2018, using the fractal property of the Shanghai stock market and employing the contribution of algorithm of fractal interpolation and Support Vector Machin (SVM), have focused on predicting price patterns [21]. Algorithm 1 Wang Algorithm:Require: Gold closing price.Ensure: Prediction of gold price pattern in a short-term time interval.Start1: Examine the stability and fractal structure of gold price data using the Hurst exponent.2: Predict the data using the SVM algorithm.3: Adjust the points obtained from Step 2 using the fractal interpolation algorithm.4: Predict a short-term period based on the corresponding fractal interpolation function of the pointsfrom Step 3.End Algorithm 2 Fracsion Algorithm:Require: Gold closing price in a priod.Ensure: Prediction of gold price pattern in a short-term time interval.Start1: Examine the stability and confirm the fractal structure of the data using the Hurst exponent.2: Generate a set of fractal points for data with a fractal structure.3: Call and train the SVR algorithm based on the obtained fractal data.4: Predict the trend of gold price in a time interval based on the regression function obtained in Step 3.End The results for 2022 are presented in the following figure, for instance.The comparison of the results indicates that although both algorithms exhibit errors in price prediction, the adaptive Fracsion algorithm outperforms the Wang algorithm in predicting the price trend of gold in a short term memory. 3- Summary of Proofs/ConclusionsIn this paper, we analyzed the gold price time series in two phases. Firstly, by applying Hurst's method for each year, we investigated the stability of the gold price pattern (fractal structure of the system). Secondly, we utilized existing algorithms on the time series corresponding to the price of gold to predict its price trend, which provides important information to investors who seek to predict the gold market. The fundamental analysis of the gold market with the fractal structure presents a new approach to the analysis of the gold market and a non-linear perspective on this issue. The characteristic of long-term memory as well as the fractal structure of a time series corresponding to gold price data can play an effective role in predicting gold fluctuations and hence, the return based on gold fluctuations. However, predicting the price trend of the gold market in the long term is difficult due to many different factors that affect the global markets. Nevertheless, using algorithms for predicting the behavior of a time series, as a technical analysis tool, in stable economic and political conditions can have satisfactory results in predicting the price trend of gold in the short term. However, when there are conditions that strongly affect the price trend, such as war, global inflation, and in recent years, the global pandemic of Covid-19, algorithms cannot perform well in predicting the price pattern trend, because the structural order of the price pattern in such a situation, it is faced to distribution.

Background: Climate change and declining rainfall have significantly reduced access to surface water in Iran, particularly in Hamedan Province. This has led to increased reliance on groundwater resources, consequently altering their quality for various uses. Therefore, this study was conducted to develope a qualitative model for assess of groundwater resources in Kabudarahang Plain, Hamedan Province, using an Artificial Neural Network (ANN) and the Gray Wolf Optimizer (GWO) algorithm. Methods: Qualitative data of groundwater resources in Kabudarahang Plain were collected and analyzed over a decade (2012-2022). ANN modeling was employed to predict groundwater quality changes. Additionally, the GWO algorithm was integrated to enhance prediction accuracy. The model utilized three output or dependent variables (TDS, EC, and pH) and six input or independent variables (calcium, magnesium, chloride, sulfate, sodium, and turbidity). Results: : The ANN model demonstrated that over 99% of water quality variations can be attributed to the six input variables. Moreover, the GWO algorithm effectively reduced average prediction errors from 0.0015 to 0.0008. Conclusion: The ANN algorithm exhibited high prediction accuracy, low prediction error, and model optimality, which were further enhanced by the GWO algorithm. This suggests that while the ANN model successfully predicted groundwater quality changes in the study area, the GWO algorithm refined the predictions, and improving the model's overall performance. Considering the complementary nature and effectiveness of ANN and GWO algorithms for prediction, their application to predict qualitative changes in groundwater resources in other regions is recommended.

This research was conducted to investigate the energy balance and global warming potential of almond production through by traditional and mechanized cultivation in Lenjan county, Isfahan province. Information related to the consumption of inputs and outputs was collected using 175 and 177 questionnaires in traditional and mechanized cultivation, respectively. Equivalent energy of inputs and outputs, as well as global warming potential, were calculated using coefficients of equivalent inputs. The results showed that the total energy consumption in traditional and mechanized cultivation is 14108.53 and 13767.39 MJ ha -1, energy efficiency is 0.19 and 0.24, and energy efficiency is 0.10 and 0.12 kg/MJ, respectively. Statistically, a significant difference was observed between the energy intensity index and energy consumption in both methods (P≤ 0.05). However no significant difference was observed between the energy intensity index in these two methods. Based on the economic analysis, the profit-to-cost ratio in traditional and mechanized cultivation was 3.27 and 4.2, respectively. The global warming potential from traditional and mechanized cultivation was 533.23 and 672.3 kg equivalent of carbon dioxide per hectare. The highest levels of environmental pollution in traditional and mechanized farming were associated with diesel fuel, accounting for 64.46% and 83.83%, respectively.

Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network. Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders. Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results. Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.

In order to the shelf-life prediction of white shrimp fillet (Litopenaeus vannamei) using Arrhenius mathematical model and Artificial Neural Network at different temperatures (-15, -25, -35, -45 °C), the qualitative changes of the fillet including salt extractable protein (SEP), K index, total volatile nitrogen bases (TVB-N), peroxide index (PV), barbituric acid (TBARS), electrical conductivity (EC) and sensory evaluation (SA) were investigated. For the Arrhenius model, the relative error range between the measured and predicted values for the quality factors i.e. TVB-N, SA, EC, TBRAS, K and SEP was -73.17-15.12, -2.54-13.04, -6.47-1.62, -0.81-0.00, -25.99-2.02, -5.59-0.82%, respectively. Regarding to Artificial Neural Network model, the relative error range between the predicted and measured values for the quality factors TVB-N, SA, EC, TBRAS, K and SEP were 0.00, 0.00, -0.38-0.00, 0.00, 0.00 and -0.08-0.03%, respectively. The MSE values of the Artificial Neural Network model were lower than the Arrhenius model in most of the qualitative factors. The R2 of the frozen shrimp quality factors of the Artificial Neural Network model was higher than the Arrhenius model, except for the SA factor. The artificial neural network model was able to better show the trend of changes in the quality of shrimp stored during the 6 months of the freezing period, at of -15 to -45 °C, compared to the Arrhenius model.

The city of Tabriz, which has the fastest urban growth in the northwest of the country, is one of the largest cities in Iran in terms of population, economic activity, industry and transportation options. Public transportation and industry combustion and lack of proper filtration of these industries, such as thermal power plants, has led to increased air pollution in the city. For this purpose, the present study tries to use input variables (distance from industrial centers, humidity, temperature, population density, distance from commercial centers, distance from bus stations, distance from educational centers, vegetation changes, distance from free Roads, building density, wind direction, carbon dioxide and carbon monoxide) to assess air pollution using artificial neural networks in the metropolis of Tabriz. In the present study, the independent variables affecting the distribution of pollution probability in two models of multilayer perceptron neural network (MLP) and linear regression were tried to be defined by defining measures in urban management and influencing and planning the mentioned variables.‌Improve pollution control.The results show that the major pollutants are mostly suspended particles (PM10), gas (CO2), (SO2) and (NOx).The dispersion of airborne particles is mostly‌due to vehicle traffic, industrial activities, fuel combustion of diesel engines and construction and the need to generate more electricity.-The activities of thermal power plants, Tabriz refinery and domestic and commercial heating systems are also among the factors producing SO2 and the highest CO2 production is related to the fuel of gasoline-burning vehicles.‌The intensity of the increase in the amount of this pollutant in all selected stations in the autumn and winter seasons is much higher, so that in these seasons the pollutants reach more than twice the allowable level.The share of Tabriz air pollutants can be divided into three general categories, the most important of which is the thermal power plant and transportation.

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%.

Indiscriminate exploitation of groundwater has caused a decrease in the groundwater level and as a result, has caused land subsidence in many areas. This problem is especially visible in arid and semi-arid regions like Iran, where water supply for agriculture, drinking, and industry is done from groundwater water sources, and in recent years, it has seriously threatened the aquifers of the plains as a serious danger. In this research, due to the importance of the problem, land subsidence in the Damghan Plain aquifer located in Semnan province was studied and investigated.In this research, the amount of subsidence was measured at the field, and then its spatial changes were investigated using conventional methods such as Kriging interpolation, Co-kriging, and inverse distance weighted interpolation (IDW). Also, the artificial neural network model was used to estimate and interpolate the amount of subsidence. Three statistical indices namely the coefficient of correlation (R2), the root mean square error (RMSE), and the mean absolute error (MAE) were used to compare the estimation of subsidence values using an artificial neural network model, kriging interpolation method, cokriging, and IDW. To perform interpolation using kriging, cokriging, and IDW interpolation methods, the variogram of land subsidence data was drawn. Also, in order to increase the accuracy of the mentioned models in predicting the amount of subsidence, the auxiliary variable of water level reduction was used.Using different functions such as circular, spherical, exponential, Gaussian, and linear models for plotting the semivariogram, results show that the Gaussian function with segment-to-threshold ratio (C0/(C0+C)) equal to 0.26 has better performance compared to other models. Also, the artificial neural network has a better performance compared to the kriging method and the inverse weighted distance method and has been able to reduce the RMSE error value in the validation stage by 17.6% and 31.3%, respectively. It has also increased the value of the R2 from 0.502 and 0.421 to 0.721.The use of the auxiliary variable of water level reduction has increased the accuracy of the models used in predicting the amount of subsidence. In this case, the comparison between the estimation of subsidence values using the artificial neural network model compared to the interpolation method of kriging, cokriging, and IDW shows that the artificial neural network model with a coefficient of determination (R2) of 0.860 and 0.751 and 0.015 and 0.017, has a better performance compared to the mentioned methods and reduce the amount of prediction error. Therefore, the artificial neural network model can be used with good accuracy as an alternative method instead of conventional interpolation methods to investigate the spatial changes of land subsidence.In recent years, the phenomenon of land subsidence has occurred in many plains of Iran due to excessive extraction of underground water, including Damghan Plain. In this research, by using the measured data of land subsidence in this plain, its spatial changes were investigated using kriging and cokriging methods (geostatistics method), inverse distance weighted method (IDW), and weighted IDW (deterministic method) in the whole plain. it placed. The research results showed:• Compared to circular, spherical, exponential, and linear functions, the Gaussian function can better estimate the spatial changes of land subsidence.• The results of the kriging method were better than the inverse distance weighted (IDW) method.• The artificial neural network model with Gauss function and two intermediate layers has better performance than other transfer models such as sigmoid, hyperbolic tangent, and hyperbolic Bade secant.• The use of artificial neural network model has increased the accuracy of land subsidence estimation compared to conventional methods such as the kriging method and inverse weighted distance method.

In order to diagnose breast cancer, methods such as mammography, MRI, thermal mammography and detection with a simple breast health test device (Brest Angel) are used. Different image processing methods are among the effective methods for detecting different types of tumors in women's breasts. In this article, two types of artificial neural networks are used. 5 statistical features extracted from thermographic images of women's breasts were used to diagnose cancer in neural networks. In this article, back propagation neural network (network 1) is used with Lunberg-Markudat training method and its results are compared with hybrid back propagation-wavelet network (network 2) to investigate the condition of women's breasts. The outputs of the two neural networks used in the article have 2 nodes, which indicate whether the person in question has breast cancer or not with the information given to the neural networks. In network (1), correlation coefficient (R=0.9831) and root mean square error (RMSE=0.5538) were obtained as the best function for network training. In contrast to the network correlation coefficient (2), R=0.9945 and root mean square error (RMSE=0.4665) was obtained. The training time of neural network 1 was 45.51 seconds and network 2 was 33.68 seconds. The results of the designed wavelet-back propagation hybrid neural network show that the proposed network is effective in detecting breast cancer with 99.5% accuracy and is able to detect the health status of women's breasts.

Producing mineral potential model using GIS software has been increased over the past years. In this study, predictive map consisted of argillic alteration, philic alteration, iron oxide alteration, reduction to pole of aeromagnetic data, lineaments, cu geochemistry anomaly, and principal component analysis (component 3) were prepared from Shahre Babak area. For training model, 37 mineralized points were used. Point pattern analysis was used as well for making non-deposit points and for training model, percepteron artificial neural network with two layers was applied. The training model was used to prepare the final mineral potential model. Based on the mentioned model, the main promising areas were identified to be in the northwest and eastern part of the studied area. Moreover, two areas in the northern and southwestern parts of this area were identified for additional studies. For evaluating the model, ROC curve was used. ROC curve shows high precision of the produced model. For more evaluating, sensitivity, specificity, positive predict value, negative predict value, accuracy, and kappa were computed. The coefficients confirm the high accuracy of the mineral potential model. Introduction Mineral prospectivity mapping (MPM) is a multicriteria decision-making task that aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of the type sought (Carranza and Laborte, 2015; Yousefi and Carranza, 2015; Sun et al., 2019). In the early stages of exploration, if there are enough known indices in an area, data-driven modeling is proper for mineral potential prospectivity. In this method, at first, all the characteristics of the known indices, of the type of mineralization sought, are collected and the relationship of these characteristics with evidence and spatial patterns is quantified. Then, points with similar characteristics are searched in those areas. Shahre Babak as the studied area is a part of Urumieh-Dokhtar zone. Urumieh-Dokhtar zone is proper for porphyry copper deposits. In this study, at the first stage, conceptual model was defined for porphyry copper modelling. Then, based on the model, some predictive layers were made ready and the data were imported to the trained model of artificial neural network in MATLAB 2021. At the next stage, final model was presented. Material and methods For constructing mineral potential model, a conceptual model was defined. Based on this model, some predictive layers consisted of argillic alteration, iron oxide alteration, phillic alteration, reduction to pole of airborne magnetic map, cu geochemistry anomaly, principal component geochemistry anomaly, intrusive units, lineaments structures, and digital elevation models were made in ARCGIS in raster formats. The pixel size of the raster files is 100m*100m. After fuzzification of raster files, these features were extracted to ASCII formats. Geology data (Intrusive body, faults, and dykes) Shahre Babak geology map in 1:250000 scale was used for extracting geological information. The intrusive bodies, faults, and dykes were extracted from Shahre Babak geology map. After extracting geological information, based on the Euclidean distance, the distance maps were made in ARCGIS. Then these maps became fuzzy. Airborne magnetic data The airborne magnetic data were surveyed by Atomic Energy Organization of Iran (AEOI) during 1977 and 1978. The flight lines distance and the sensor altitude were about 500 and 120 m, respectively. The reduction to pole filter was applied on total magnetic intensity map. Geochemistry data Geochemistry data in 1:250000 scale was used for geochemical interpretations. The cu geochemistry anomaly was drawn from the data. Principal component analysis method was applied on geochemical data. Component 3 was extracted from the data. Aster data Band ratio method was used for extracting the alterations. Iron oxide alteration, philic alteration, and argillic alteration were drawn in ENVI software in raster format. The iron oxide, argillic, and philic alteration files were imported to ARCGIS software and transformed to shapefile format. The distance maps were drawn based on the Euclidean distance. Then these maps became fuzzy. Digital Elevation Model Digital Elevation Model (DEM) was extracted from Aster data. The data became fuzzy. Training dataset For training model, 37 deposit points were selected. Point pattern analysis was used for non-deposit points. Based on this method, 37 non-deposit points were extracted of the Shahre Babak (the studied area). Each of the labels was located in a unique pixel. The features of these points were extracted from the predictive maps. Then these points were imported to artificial neural network (perceptron neural network with two layers). 70% of data were used for training model and 30% were used for testing model. Then the trained model was applied on the ASCII format. The resulting model was drawn using ARCGIS. Artificial neural network ANN is a modelling approach that simulates human brain system inspired by biological neural networks (Celik and Basarir, 2017). ANN can be effectively applied for pattern recognition in a wide variety of geoscience investigations. In this network, the neurons of different layers are interconnected to exchange information in a unidirectional way starting from the input layer through hidden layers to the output layer (Rodriguez-Galiano et al., 2015; Celik and Basarir, 2017). The flow of information is performed by assigning weights to the connections of different neurons (Rodriguez-Galiano et al., 2015). The back-propagation algorithm is employed to ensure the learning capability of ANN. This algorithm computes the error between the outputted value and real target value, then feeds back it to ANN in order to adjust the weights and biases (Celik and Basarir, 2017). Results Mineral potential map of studied area was produced by artificial neural network. Based on resulting model, the first-class promising areas were detected in north western and eastern parts of the studied area. Moreover, two areas in north and south western parts of studied area were identified. For evaluating the model, ROC curve was used. This curve shows model accuracy with high precision. For further evaluation, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and kappa were calculated with 94.7%, 91.8%, 92.3%, 94.4%, 93.3%, and 89%, respectively. These coefficients also confirm the high accuracy of the mineral potential model.

The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice.

Introduction Indiscriminate exploitation of groundwater has caused a decrease in the groundwater level and as a result, has caused land subsidence in many areas. This problem is especially visible in arid and semi-arid regions like Iran, where water supply for agriculture, drinking, and industry is done from groundwater water sources, and in recent years, it has seriously threatened the aquifers of the plains as a serious danger. In this research, due to the importance of the problem, land subsidence in the Damghan Plain aquifer located in Semnan province was studied and investigated. Methods In this research, the amount of subsidence was measured at the field, and then its spatial changes were investigated using conventional methods such as Kriging interpolation, Co-kriging, and inverse distance weighted interpolation (IDW). Also, the artificial neural network model was used to estimate and interpolate the amount of subsidence. Three statistical indices namely the coefficient of correlation (R² ), the root mean square error (RMSE), and the mean absolute error (MAE) were used to compare the estimation of subsidence values using an artificial neural network model, kriging interpolation method, cokriging, and IDW. To perform interpolation using kriging, cokriging, and IDW interpolation methods, the variogram of land subsidence data was drawn. Also, in order to increase the accuracy of the mentioned models in predicting the amount of subsidence, the auxiliary variable of water level reduction was used. Results and discussion Results show that the Gaussian function with segment-to-threshold ratio (C0/(C0+C)) equal to 0.26 has better performance compared to other models. Also, the artificial neural network has a better performance compared to the kriging method and the inverse weighted distance method and has been able to reduce the RMSE error value in the validation stage by 17.6% and 31.3%, respectively. It has also increased the value of the R² from 0.502 and 0.421 to 0.721. In this case, the comparison between the estimation of subsidence values using the artificial neural network model compared to the interpolation method of kriging, cokriging, and IDW shows that the artificial neural network model with a coefficient of determination (R² ) of 0.860 and 0.751 and 0.015 and 0.017, has a better performance compared to the mentioned methods and reduce the amount of prediction error. Therefore, the artificial neural network model can be used with good accuracy as an alternative method instead of conventional interpolation methods to investigate the spatial changes of land subsidence. Conclusion The research results showed: Compared to circular, spherical, exponential, and linear functions, the Gaussian function can better estimate the spatial changes of land subsidence. The results of the kriging method were better than the inverse distance weighted (IDW) method.The artificial neural network model with Gauss function and two intermediate layers has better performance than other transfer models such as sigmoid, hyperbolic tangent, and hyperbolic Bade secant.The use of artificial neural network model has increased the accuracy of land subsidence estimation compared to conventional methods such as the kriging method and inverse weighted distance method. [ABSTRACT FROM AUTHOR]

This study aimed to produce tomato paste using natural preservatives as an alternative to chemical preservatives. Fennel seed extract (Foeniculum vulgare Mill) and Ziziphora clinopodioides Lam. were used as natural preservatives.Physicochemical properties of tomato paste such as pH, acidity and total soluble solids (Brix) were measured during 5 weeks of storage at 4°C. Sensory properties of tomato paste were assessed using trained and untrained sensory evaluators using a 5-point hedonic scale.To predict the data from an artificial neural network of topology 2-37-1, storage period and different concentrations of fennel seed extract and Ziziphora clinopodioides Lam. were considered as inputs and acidity as the target parameter.The results showed that using different concentrations of fennel seed extract and Ziziphora clinopodioides Lam. significantly reduced (p <0.05) the pH and increased the acidity of tomato paste.Increasing the level of extracts in tomato paste samples significantly (p <0.05) increased Brix during storage period.Sensory evaluation showed that in terms of color, appearance and consistency treatments containing low concentrations of the extracts (0.5 and 1% of both extracts) scored higher.In terms of taste and smell, treatments 3 and 4 (containing 2 and 3% of fennel seed extract, respectively) had the highest score.In general, it can be concluded that using 2 or 3% fennel seed extract as a natural preservative in tomato paste leads to desirable physicochemical and sensory properties.The results of acidity prediction showed that the correlation coefficient and the mean squared error for the total data were 0.99232 and 0.00002, indicating a successful prediction. [ABSTRACT FROM AUTHOR]

This research was conducted to investigate the energy balance and global warming potential of almond production through by traditional and mechanized cultivation in Lenjan county, Isfahan province. Information related to the consumption of inputs and outputs was collected using 175 and 177 questionnaires in traditional and mechanized cultivation, respectively. Equivalent energy of inputs and outputs, as well as global warming potential, were calculated using coefficients of equivalent inputs. The results showed that the total energy consumption in traditional and mechanized cultivation is 14108.53 and 13767.39 MJ ha -1, energy efficiency is 0.19 and 0.24, and energy efficiency is 0.10 and 0.12 kg/MJ, respectively. Statistically, a significant difference was observed between the energy intensity index and energy consumption in both methods (P< 0.05). However no significant difference was observed between the energy intensity index in these two methods. Based on the economic analysis, the profit-to-cost ratio in traditional and mechanized cultivation was 3.27 and 4.2, respectively. The global warming potential from traditional and mechanized cultivation was 533.23 and 672.3 kg equivalent of carbon dioxide per hectare. The highest levels of environmental pollution in traditional and mechanized farming were associated with diesel fuel, accounting for 64.46% and 83.83%, respectively. [ABSTRACT FROM AUTHOR]

The ability to predict tool wear during machining is a very important part of diagnosis, which makes it possible to replace the tool at the appropriate time. Therefore, in this research, the artificial neural network approach was used to predict tool wear. First, hardened steel 4140 was turned with uncoated cemented carbide tool TCMW 16T304 H13A and with input parameters including cutting speed, feed rate and machining time in three different levels and with constant cutting depth, and the amount of tool wear was measured. And the experimental test results were used to train and validate the artificial neural network. The optimal neural network architecture was obtained with 3 nodes in the input layer, two hidden layers with 12 and 36 nodes in the first and second hidden layers, and 1 node in the output layer to predict tool wear. The prediction values of the artificial neural network model were compared with the experimental results and the average error percentage of the validation data was calculated as 3.32%. [ABSTRACT FROM AUTHOR]

Objective: Organizational trauma management is a neglected public health crisis. While it seems that it is a very important issue that should be investigated from different factors. The present study was conducted with the aim of finding the relationship between the role of organizational freshness and psychological empowerment in the management of new teachers' organizational trauma with an artificial neural network (ANN) approach. Method: The statistical population of this study included nine teachers of Gilan, Alborz and Tehran provinces with a population of 15,000 people in 2011 (with 1 to 3 years of work experience). The sampling method was a multi-stage cluster type. The sample size was considered to be 375 according to the Krejci-Morgan model. To collect data, Kroloff's Organizational Vitality Questionnaire (2007), Sportiz Psychological Empowerment Questionnaire (1998) and the researcher's Organizational Trauma Management Questionnaire (1401) were used. The validity of the instruments was confirmed by the opinion of professors of educational sciences and psychology. The data was analyzed with Multi Layer Perceptron (MLP) artificial neural network in Spss.25 software. The results showed that the relationship between organizational vitality and psychological empowerment on organizational trauma management has an input layer with 13 nodes and a hidden layer with 4 nodes, and the artificial neural network is well able to detect the jumps and process of organizational trauma management from vitality. Predict organizational and psychological empowerment. Findings: The findings show that the feeling of participation with others (0.100) has the most influence and the meaningfulness of work (0.17) has the least influence on the coefficient of importance in organizational trauma management. Conclusion: According to the studies, organizational trauma is an incident that affects the ability of the organization and can lead to the loss of organizational talents. Usually, these shocks are the result and cause of the malfunction of one or more pillars of an organization. In other words, the artificial neural network method used in the research showed that if the two factors of organizational vitality and psychological empowerment are given less importance in the organization, the control of organizational management will be more difficult in the organization. [ABSTRACT FROM AUTHOR]

The main goal of the current research is to predict the ethical purchase intention of sustainable products in the circular business model through the behavior of customers/tourists using artificial neural network (ANN). The statistical population of the present study are customers/tourists who have used the products/services of Gargop restaurant. Sampling was done based on Morgan's table and 384 people were examined through a questionnaire. The results indicate that as much as the input variables include attitude (willingness to pay more money, attitude towards circular business models), perceived value (social value, functional value and value) and awareness (awareness of ethical product/service and brand awareness) have a higher value; it is expected that the ethical purchase intention will be stronger. The present study by examining the behavioral factors of customers to the collection of literature related to circular business models and also sustainable development helps.IntroductionTourism has many positive and negative economic, social, cultural, and environmental effects. These dimensions have always been and will be discussed in most sources as essential areas of destination sustainability in sustainable development issues. In the world, the tourism industry is known as a smoke-free industry. According to those mentioned above, the expectation from this industry without smoke is that all these active businesses will operate in line with sustainable development goals. On the other hand, the rapid growth of the world's population and urbanization increases consumption significantly, while natural resources are limited and scarce (De Angelis, 2018). The world's population is estimated to reach about nine billion people by 2050, and it consumes resources more than three times the current rate (Planning, 2015). The current linear economy, characterized by "take-produce-do things in order," accelerates the depletion of resources and energy (Boken et al., 2017).The concept of circular or circular economy, inspired by nature in the late 1970s, means that nothing is wasted and all outputs are inputs to other processes (Ellen McArthur, 2018). The circular economy is a strategy to oppose the traditional market system, aiming to deal with the challenge of lack of resources and waste disposal in a win-win approach with an economic and value perspective (Homrich et al., 2018). The ethical purchase intention of customers/tourists is essential for the successful implementation of circular business models and sustainable development. However, few researchers have focused on the role of customers/tourists in activating circular business models and sustainable development.The current research fills this gap by identifying the essential factors that affect the ethical purchase intention of customers/tourists for circular business models and sustainable products/services in the tourism industry in restaurants and catering centers. According to the research of researchers of this article, no research has been done on the mentioned subject in this way in Iran, so the value and innovation of the present research are also evident. Therefore, the main problem of the current research is that only some customers or tourists may buy and use sustainable products/services; for example, tourists interested in using luxury restaurants may need to be more attractive to them. In this regard, the main goal of the current research is to identify the behavioral factors that determine customers'/tourists' intention to purchase sustainable products in circular business models ethically.For this purpose, artificial neural network (ANN) has been used as a prediction method, and Python software has been used for analysis. The network's output in this research is ethical purchase intention, and its inputs are the behavioral factors of customers/tourists. The statistical population of this research is the customers/tourists who use the products/services of Gargop restaurant, the first wooden recycled restaurant in Iran, which has all its facilities, products, and services in line with sustainable development near Khorkhoran International Wetland in the coastal city of Bandar Khmeir, Hormozgan province. Sampling was done based on the table of Morgan and Krejcie, and 384 people were examined through a questionnaire. Materials and MethodsThe present research is quantitative; firstly, primary information was collected using a questionnaire, then analyzed using an artificial neural network (ANN) prediction method and Python software. The output of the network in this research is ethical purchase intention. Its inputs are the behavioral factors of customers/tourists, including attitude (willingness to pay more money - attitude towards circular business models), perceived value (social value - functional value - cognitive value) is awareness (biological awareness - environmental awareness - awareness of ethical product/service - brand awareness).The statistical population of this research is the customers/tourists who use the products/services of Gargop restaurant, the first wooden recycled restaurant in Iran, which has all its facilities, products, and services in line with sustainable development near Khorkhoran International Wetland in the coastal city of Bandar Khmeir, Hormozgan province. Sampling was done based on the table of Morgan and Krejcie, and 384 people were examined through a questionnaire.Discussion and ResultsThe specifications of the model are as follows:Due to the lack of balance of the data classes, that were 339 in category one (ethical purchase intention) and only 45 in category zero (no ethical purchase intention) the SMOTE algorithm (Chawla et al., 2002) have used to solve this problem and the increase of the data of the lower class (not intending to buy ethically). Therefore, the neural network was built with an equal number of 339 in class one (ethical purchase intention) and 339 in class zero (no ethical purchase intention). The data were divided into three categories: 70% training, 20% testing, and 10% validation. ConclusionsAccording to the research results, when x variables include attitude (willingness to pay more money (x1), attitude towards circular business models (x2)) - perceived value (social value (x3), functional value (x4) and cognitive value (x5)) and awareness (awareness of ethical product/service (x6) and brand awareness (x7)) get a higher value, variable y means ethical purchase intention also increases, gives that, of course, this relationship is not linear and is formed in the form of a network, and the hidden layer is also essential. By examining the issue from the customers' point of view, the present research contributes to the collection of literature related to ethical purchase intention and circular business models, and consequently to sustainable development, especially in the restaurant and tourism industry, and it provides valuable guidelines for businesses to be more successful in the current market. Considering that businesses have shifted from a linear economy to a circular economy, the vital role of customers/tourists should not be ignored.Therefore, understanding the essential behavioral factors of customers can pave this path, and by predicting their ethical purchase intention through these important behavioral factors, it is possible to predict their behavior and influence their behavior change because the behavior is not formed by itself and is the result of the customer's intention to buy ethically. The current research expands the role of customers and explains how different factors can collectively affect customers' acceptance and purchase intention of the circular business model. Business managers can apply the circular business model in their business and adjust the type of their offers according to the customers they want to target. The present research results help them better understand the behavior of customers who pay more attention to sustainable development goals and are concerned in some way.The intention (intention) and customers' buying behavior are essential to implement the circular business model successfully. Customers' different personal characteristics and changes over time make it difficult for businesses to fully understand their expectations and behavior. This is even more important given the drastic changes from linear to circular economies. Therefore, businesses should do deep market research about their target customers in different time frames and consider many characteristics in a categorized manner. Finally, to successfully brand and protect the brand, as a business that operates with sustainability goals, it should take special measures and stabilize this brand in customers' minds in the long term.

In order to make decisions for regional planning and achieve sustainable development, it is necessary to quantify land use and land cover changes. In this study, the land use and land cover maps of the Zayandehrood Dam watershed were prepared for the period of 1991 to 2021, using Landsat satellite images, and the changes that occurred in this period were revealed. Using the land change modeler (LCM), land use and land cover and their future changes for 2051 were modeled and predicted. The results showed that in the period between 1991 and 2021, the coverage of poor rangelands with 51,871 hectares of change had the largest decreasing trend, and the agricultural class had the largest increasing trend with 71,478 hectares of change. The largest decline in the period from 2021 to 2051 is related in the coverage of the fair rangelands class with 66192 hectares, and the agricultural class potentially has the largest increasing trend with 70328 hectares of change. The findings of this research will be useful for policymakers and planners. They can use the findings of this study for spatial planning in the region, managing the process of land use and land cover changes for sustainable development.

New performance-based design approaches in earthquake engineering aim to accurately and transparently assess the risk of loss of life and structural damage. Advanced analytical models are used to determine the performance of structures, with one of the key components being the deterioration of structural members under seismic loads. Multilinear backbone curves are commonly used in regulations and software to simplify the behavior of members subjected to seismic loads, including the deterioration components. This paper proposes using machine learning models to predict the deterioration components of reinforced concrete (RC) columns. A dataset of 255 experimental data from 1973 to 2002 is used to predict the deterioration components using different machine learning methods. The RC columns have three failure modes: bending, shearing, and bending-shearing. The deterioration components predicted by the analytical relationships are compared with the results obtained from machine learning methods. The dataset includes 14 features as model inputs and 3 features as outputs. The paper examines three algorithms for predictions: AdaBoost, artificial neural network (ANN), and random forest (RF). The analysis is conducted using Python software. The results show that the random forest model has an accuracy rate of 91% for the Plastic chord rotations from yield to cap , 81% for Post-capping plastic-rotation capacity from the cap to point of zero strength , and 88% for normalized energy dissipation capacity compared to other algorithms. Also, the results obtained from the predicting models have considerable accuracy compared to analytical relationships. Compared to analytical models, the random forest model has significantly been improved in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient determination (R2). These improvement are 79% , 75.3% and 46.5% in (R2), 63.7% , 48.5% and 86.7 in (RMSE), 64% , 92% and 89.4% in (MAE).The results showed that the random forest model has been significantly improved the accuracy of determination of deterioration components compared with analytical models.

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

Introduction and Objective: In recent years, the increases in population and expansion of settlements in hazardous areas have greatly increased the impact of natural disasters in industrialized and developing countries. Landslide risk zoning helps to identify strategic points and geographically prone critical areas. Therefore, measures for rapid, safe mitigation and strategic planning for the future are important. In fact, landslide risk assessment may be a suitable and cost -effective help for land use planning, so in this regard, the aim of the current research is to model the risk of landslides using non -parametric data mining in watersheds of Hyrcanian forests. Material and Methods: For this purpose, the map of landslide risk according to the Mora and vahrson method with the effect of factors affecting the occurrence of landslides including topographical factors, hydrological and climatic factors, geological factors, land cover factors, human factors, hydrographic network from digital elevation model, geological map It was prepared using the map of the Geological Organization of the country, and to obtain the elevation index and the relative height, first, using the curve of height levels taken from the 1:25000 topographic maps of the region, the map of the elevation classes was prepared. After that, the study area was divided into one -square -kilometer grids, and maps with the lowest and highest elevations in one -square -kilometer grids were obtained; in the last step, by subtracting these two maps, a map was obtained whose information shows the value of the postal index and relative height. Monthly rainfall was also used to obtain the soil moisture index. Finally, algorithms of three random forest models, an artificial neural network, and a decision tree algorithm were used to model the risk of landslides in the STATISTICA 12.0 software environment. Results: According to the results, the highest distribution of landslide areas belongs to the low - risk class (76%). TP, SL, SR, and TS variables were considered the essential factors of landslide occurrence based on their importance. The results of validation with three algorithms of RF, CART, and ANN showed; According to the coefficient of explanation obtained in modeling the risk of landslides, the artificial neural network model with (R 2=0.99) is more accurate than other methods. Conclusion: The results of the present study showed that data mining methods have a high capability in predicting the risk of landslides. Therefore, the use of the mentioned methods can be considered in reducing the risks associated with landslides and planning for land use. [ABSTRACT FROM AUTHOR]

Wetlands are vital for human survival and constitute 40.6% of the total value of global ecosystem services. This study analyzed land cover changes in the Golpayegan Shoor wetland from 1988 to 2018 and then modeled it with a combination of artificial neural network, Markov chain, and multi-objective optimization for 2030 and 2040 for obtaining environmental sustainability. To obtain the highest accuracy in transition potential modeling, four scenarios were used with various calibration periods and sub-models. The FOM index was used to evaluate the accuracy of various scenarios using the approach by overlaying three maps for the years 2008, actual 2018, and predicted 2018 maps. Effective variables in describing the changes occurred in the case study selected using Cramer's v. The highest accuracy was shown in a scenario with the calibration period of 1988-1998 and 9 sub-models in land cover change modeling 2018 year. The evidence likelihood of change map and distance from agricultural lands showed the highest impact in the changes of the study area. Residential areas and agricultural lands increased by 0.6% and 1.8% in 2030, while 1% and 2.2% grew up in 2040 compared to 2018, respectively. Dense rangeland decreased by 13.1% and 18.6%, respectively in 2030 and 2040 compared to 2018. The water bodies showed an increase of 0.18% and 0.27% due to the conversion of agricultural areas and rangeland to Lake Golpayegan and Kochery dams. Taking water from unauthorized wells, reducing the flow of water entering the wetland, and the presence of decorative ore has caused serious problems for Golpayegan Shoor Wetland, which requires managers and land planners to consider compensatory ways to restore, water requirement, and ecological sustainability for the wetland. [ABSTRACT FROM AUTHOR]

It is common to use different data mining methods in drought prediction. However, the selection of the best model is mainly based on the accuracy of the simulation, while most of the studies do not mention the features of the models. In this paper, the performance of the most common data mining models, including Multilayer Perceptron Artificial Neural Network (ANN-MLP), Radial Base Function Neural Network (ANNRBF), Regression Decision Tree (CART), Model Tree (M5P), and Support Vector Machine (SVM) is evaluated in order to predict monthly one year ahead rainfall at Bandar Abbas synoptic station and then the characteristics of each of them are described. Calibration and validation of the models were done using raw data and a three-year moving average of climatic parameters from 1347 to 1396. The performance of the models has been evaluated using different statistical indices and comparative diagrams. The results showed that the SVM and M5P models have good prediction performance with RMSE of 7.93 and 8.31 mm, the MAE of 3.66 and 4.69 mm, and the CC of 0.83 and 0.82, respectively. Also, with the exception of the CART, the change in the data mining tool makes an eight to 11 percent difference in the accuracy of the estimates. Therefore, the most appropriate model should be selected based on other characteristics of the methods besides their accuracy. In addition, using the three-year moving average of the input parameters has increased the correlation coefficient by about 78 percent and reduced the RMSE by about 63 percent. The analysis of the longterm drought situation showed that with the increase in the period of the standard precipitation index, the separation of wet and dry years becomes more specific. [ABSTRACT FROM AUTHOR]