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This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement: The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty. [ABSTRACT FROM AUTHOR]

In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models. [ABSTRACT FROM AUTHOR]

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The water advance time (Ta) is needed for designing and evaluating surface irrigation systems. This study employed artificial neural networks (ANNs) and gene expression programming (GEP) techniques for estimating the water advance time in the border irrigation system as a function of inflow rate per unit width (Qb), length of water advance in the border (L), longitudinal slope (So), final infiltration rate of the soil (fo) and Manning roughness coefficient (n). The techniques were tested on field measurements from agricultural farms in three different provinces of Iran. Results showed that the ANN model was superior to the GEP model for the estimation of water advance time. The performance indicators for the ANN model were R2 = 0.966, RMSE = 7.805 min and MAE = 5.090 min, MBE = 0.312 and SI = 0.181. Results of the intelligence-based models were also compared with the WinSRFR model. Both ANN and GEP models predicted the water advance time more accurately than did the WinSRFR model. [ABSTRACT FROM AUTHOR]

Application of sediment source fingerprinting techniques to generate reliable information on sediment sources is growing, because of the global environmental importance of elevated fine-grained sediment mobilisation and delivery to rivers. Despite the application of fingerprinting at different spatial scales (e.g., land use types, geological units, and tributary sub-catchments), it has rarely been used to investigate the sediment contribution of topographic zones. The application of sediment sourcing techniques to topographic zones can be helpful in better understanding the spatial distribution of sediment sources within a catchment and the contribution of different topographic features to sediment production and transport. Against this background, this study assessed both topographic zone and tributary sub-catchment spatial sediment sources in the Kan River catchment, Iran. For this purpose, targeted sampling was performed for each classification of spatial sediment sources, and different tracers (geochemical, elemental ratios, and weathering indices) were measured for 80 surface soil samples collected in topographic zones, 20 bed sediment samples collected in sub-catchments, and 11 suspended sediment samples retrieved at the outlet of the main catchment. Three new statistical approaches were applied to identify composite signatures for spatial sediment source discrimination. These approaches combined the Kruskal-Wallis H-test with Particle Swarm Optimization (KW-PSO), Multi-Layer Perceptron (KW-MLP), and Radial Basis Function (KW-RBF) algorithms. A Bayesian un-mixing model was used to apportion the spatial sediment sources. The three composite signatures identified for elucidating the contributions from either classification of spatial sources generated consistent results. The results clarified that the median contribution from topographic zone 1 equated to it being the major spatial sediment source on the basis of topographic zones, with the contributions estimated at 57%, 49%, and 52% (5%-95% uncertainty range) based on the KW-PSO, KW-MLP and KW-RBF signatures, respectively. In terms of the tributary sub-catchments, the Sangan sub-catchment was identified as the dominant spatial sediment source, with corresponding estimated median contributions of 84%, 83%, and 97%, respectively. A combination of weathering indices and elemental fingerprint properties generated statistically robust composite signatures, providing further evidence that the former can augment more conventional tracers. The predicted source proportions can be explained by topographic characteristics from the digital elevation model of the different spatial topographic zones. Therefore, we conclude that topographic features can be an effective predictor of sediment sources. [ABSTRACT FROM AUTHOR]

Rapid urbanization has given rise to escalating land surface temperatures, climate change, and the emergence of surface urban heat islands (SUHIs) and urban hot spots (UHSs), posing significant environmental challenges. This study, situated in the dynamic urban landscape of southern Iran, leverages Landsat satellite imagery to scrutinize the repercussions of temperature escalation on the environment. Our approach harnesses a novel Urban Thermal Field Variance Index (UTFVI) in conjunction with thermal and spectral indices to gain insights into these challenges. We employ a multifaceted methodology that integrates linear regression, cellular automata (CA)-Markov chains, and advanced neural network techniques to predict land surface temperature (LST) values and associated indicators. Over the span of 2000–2019, our findings reveal a 5% augmentation in urban heat islands (UHIs), signifying an alarming temperature increase. A striking 46% of the region, as uncovered by UTFVI, falls into the most severe categories of ecological discomfort. Our analysis underscores the robust correlations between LST and critical indices, notably the Normalized Difference Built Index (NDBI) (0.96), Normalized Difference Vegetation Index (NDVI) (-0.71), UTFVI (0.98), and SUHI (0.82). Notably, our original contributions lie in the application of Artificial Neural Networks (ANNs), wherein the Multilayer Perceptron (MLP) method excels in predicting UTFVI (R2=0.96) and NDBI (R2=0.96), while the Radial Basis Function (RBF) method demonstrates remarkable accuracy in forecasting the SUHI index (R2=0.96). These achievements signify a groundbreaking advancement in comprehending the intricate dynamics of urban environmental conditions. The repercussions of increased urbanization, the proliferation of barren land, and dwindling vegetation in 2019 manifest in a marked decline in ecological quality, with a concomitant surge in temperatures within the study area. These findings underscore the pressing need for informed urban planning and sustainable practices to mitigate the detrimental effects of urban heat islands and their impact on local climates. [Display omitted] • Determining surface urban heat islands (SUHIs) using remote sensing • Applying LST index to investigate thermal pollution • Determining ecological comfort using the UTFVI index • Using the Markov and CA-Markov chain to predict LST • Appling the neural networks to predict UTFVI, SUHI, NDBI values [ABSTRACT FROM AUTHOR]

Background: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. Objective: The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms. Material and Methods: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017-2018, were analyzed by machine learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). Results: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95.55, 90.48, 100, and 95.20, 95.55, 90.48, 100, and 95.20, those of RBF 95.45, 90.00, 100 and 91.50, those of SVM 93.33, 90.48, 95.83 and 95.80 and those of MLP 90.90, 90.00, 91.67 and 91.50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. Conclusion: This investigation confirmed that ML can enhance the prediction of cervical cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing cervical cancer prevention. [ABSTRACT FROM AUTHOR]

Background: Logistic regression model (LRM) are popular in public health studies. However, most studies have found that artificial neural networks (ANNs) have better performance than other methods especially when the relationship between the response and independent variables is complex. We aimed to compare the performance of LRM and ANNs in the determination of drug abstinence predictors. Methods: The participants of this prospective study included 317 male addicts recruited through convenience sampling in five short-term residential substance use treatment programs in Southern Khorasan, the eastern province of Iran. Eligibility criteria included being in a detoxification stage and having a primary diagnosis of substance dependency disorder based on the diagnostic criteria of the DSM-IV-TR. The performance of the models was compared using the area under the ROC (AUC). Results: LRM and ANNs method correctly classified 82.2% and 84.4% of the participants as an abstinent group, respectively. The areas under ROC curve were 90 and 91.2, the kappa statistic was 0.643 and 0.688 for LRM and ANN models, respectively. Conclusions: The results showed classification performance was not significantly different. We concluded that the selection of prediction techniques for predicting drug abstinence factors depended on our background knowledge on the data pattern. [ABSTRACT FROM AUTHOR]

The most commonly used data for reservoir description are well and seismic data. Well data such as logs typically provide sufficient vertical resolution but leave a large space between the wells. Three-dimensional seismic data, on the other hand, can provide more detailed reservoir characterization between wells. However, the vertical resolution of seismic data is poor compared with that of well data. Conventionally, seismic data have been used to delineate reservoir structure; however, seismic data can be used for reservoir characterization such as porosity. Therefore, we can combine these two types of data to obtain reservoir parameters such as porosity and saturation. It is available the desired parameter (such as porosity) of the number of wells in the reservoir and seismic cube. And we are looking for the parameter estimation in the whole reservoir. To do this, there are several methods including multiple linear regression, neural networks, and geostatistical methods. Therefore, by determining the reservoir properties and correctly estimating these parameters, optimization can be performed with fewer wells, and the costs of exploration and production are reduced. In this paper, we apply these methods on the available data for an oil field in southwest Iran to obtain the porosity in a total reservoir cube, and these methods are then compared with one another. The results clearly show the superiority of neural networks compared with the other methods in estimating the reservoir parameter. The results also show that although estimation accuracy is increased significantly with the use of the geostatistical approach, this method requires that a sufficient number of well logs, representing all the fields under investigation, be provided in order to improve the geological model obtained by the multi-attribute and neural network methods. [ABSTRACT FROM AUTHOR]

Background: Human brucellosis is widespread in Fars province. The present study aimed to investigate the effect of climate on its incidence and determine the areas prone to the infection. Methods: Monthly meteorological data and the incidence rate of human brucellosis during 2009-2015 were collected and their correlation was studied using Pearson's correlation coefficient. Additionally, the multiple regression method and multi-layer perceptron neural network model were used to predict the incidence of human brucellosis. In order to analyze the data SPSS software (version 16.0), MATLAB software (version 8.1), and GIS software (version 10.4) were used. Results: Pearson's regression analysis, on a monthly basis, showed a significant indirect correlation between the incidence of human brucellosis (with a time lag of up to 5 months) and climatic parameters (minimum temperature: -0.72 and evaporation: -0.73) in Abadeh (Fars, Iran). Moreover, there was a significant direct correlation (P<0.001) between the incidence of human brucellosis and the maximum relative humidity (+0.67) and rainfall (+0.48). The incidence of human brucellosis in Abadeh was predicted by using artificial neural network models (4 layers, 4 neurons in each layer), the Levenberg-Marquardt training algorithm, and Sigmoid transfer function. It was determined that a correlation rate of 0.89 in the training level and 0.8 in the test level (with the lowest error rate) were the best values in multi-layer perceptron modeling. Conclusion: Climatic parameters are important factors in determining the incidence rate of human brucellosis in Fars province. Climate conditions provide a favorable environment for the spread of human brucellosis in this area. [ABSTRACT FROM AUTHOR]

Wind speed is an essential component that needs to be determined accurately, especially over long‐term periods for various engineering and scientific purposes including renewable energy productions, structural building sustainability and others. In this study, six different heuristic methods: multi‐layer perceptron artificial neural networks, (ANN), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), generalized regression neural networks (GRNN), gene expression programming (GEP) and multivariate adaptive regression spline (MARS) are developed to model monthly wind speeds using meteorological input information. The atmospheric pressure, temperature, relative humidity and rainfall values are obtained from Jolfa and Tabriz meteorological stations, Iran, and are used to build the proposed predictive models.. Different statistical indicators are computed to evaluate and comprehensively assess the performance of the six heuristic methods. Over the testing phase, the ANFIS‐GP and GRNN models are seen to exhibit the highest predictive performance for the Jolfa and Tabriz stations, respectively. That is, the maximum coefficient of determination are found to be 0.874, 0.858, 0.850, 0.849, 0.847 and 0.826, for the GRNN, ANFIS‐GP, ANFIS‐SC, ANN, GEP and MARS models, respectively, for Jolfa station, respectively, revealing the superiority of GRNN over the five counterpart models. The results show the generalization capability of the tested heuristic artificial intelligence techniques for both study stations, and therefore could be explored for windspeed prediction and various decisions made in regards to climate change studies. [ABSTRACT FROM AUTHOR]

This study investigates factors in tourism supply chain management (TSCM) which can influence tourist satisfaction in Isfahan, the leading tourist destination in Iran. Applying a neural network based on intelligent algorithm, the results indicate that factors such as recreation and entertainment, transportation system, tourism attractions, catering services, shopping and accommodation services failed to satisfy international tourists while tourist infrastructure and tourist information factors were quite satisfactory. It seems that TSCM in Isfahan failed to create an integration across heterogeneous sectors. This study contributes to our understanding of how TSCM is working in a mature destination in a developing country, with particular reference to Isfahan. [ABSTRACT FROM AUTHOR]

Water is a component that makes up about 60%-70% of the adult human body weight and is the basic ingredient for life after oxygen. Satisfactory and high quality water is essential for human life. Nowadays in water treatment, disinfection is considered as one of the main methods for chemical water treatment. Currently the use of chlorine for disinfection is the most common method in the world including Iran because of low cost and high germicidal power in water distribution networks and good residual effect. Dosage of chlorine is different depending on the circumstances of inlet raw water including pH, temperature, and amount of suspended solids and the injection of chemicals such as preliminary chlorine, coagulant (aluminum sulfate), and coagulant assistant (polyelectrolyte). Free residual chlorine is used to disinfect water sources and water transfer lines to the houses of citizens. In this paper, adaptive neuro-fuzzy inference system (ANFIS) was used to optimize the amount of injected secondary chlorine in clear water. For modeling, exploitation data are divided into two categories (training and testing). Eventually, the results of modeling were compared with experimental data that demonstrates fine data compliance, as correlation coefficient in neuro-fuzzy inference network was 0.95 for training data, and 0.93 for testing data. [ABSTRACT FROM AUTHOR]

A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models. [ABSTRACT FROM AUTHOR]

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data. [ABSTRACT FROM AUTHOR]

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataâthat are freely availableâgive information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions. [ABSTRACT FROM AUTHOR]

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability. [ABSTRACT FROM AUTHOR]

The offshore industry widely uses offshore pipelines to transport petroleum products due to their high capacity and reasonable installation costs. Corrosion is the leading failure cause of offshore pipelines, and their reliability analysis is necessary for reducing maintenance costs. This study presents a corrosion analysis in the bursting mode of the pipeline using a codified method based on probabilistic space theory, considering the monitored data of the oil pipeline and the pigging data of the AB-KHARK pipeline. The relationship between pigging data, parameters affecting corrosion, and the reduction of pipe wall thickness are determined based on the indicators of Regression and Neural Network machine learning methods. A new method is presented for modeling the failure modes of offshore pipelines based on their historical data and statistical computation results. Finally, the system is investigated by the first-order reliability method considering the limit state function provided by evaluating the offshore pipeline reliability. The proposed method is evaluated using a dataset from AB-KHARK island in IRAN indicate zero percentage error in failure prediction versus related works with 1% error for PRCI and MB methods, 6, 8, and 11 percentages error against RITCHE, DNV, and AMSE methods, respectively. • We estimate the probabilistic of the corroded pipelines systematically. • The relationship between ILI data parameters affecting corrosion was determined. • We evaluate the burst capacities of the corroded pipelines using the LSF model. • We analyze the reliability of corroded pipelines based on the FORM. [ABSTRACT FROM AUTHOR]

Background: Antipsychotic monotherapy or polypharmacy (concurrent use of two or more antipsychotics) are used for treating patients with psychiatric disorders (PDs). Usually, antipsychotic monotherapy has a lower cost than polypharmacy. This study aimed to predict the cost of antipsychotic medications (AM) of psychiatric patients in Iran. Materials and Methods: For this purpose, 790 patients with PDs who were discharged between June and September 2010 were selected from Razi Psychiatric Hospital, Tehran,Iran. For cost prediction of AM of PD, neural network (NN) and multiple linear regression (MLR) models were used. Analysis of data was performed with R 2.15.1 software. Results: Mean ± standard deviation (SD) of the duration of hospitalization (days) in patients who were on monotherapy and polypharmacy was 31.19 ± 15.55 and 36.69 ± 15.93, respectively (P < 0.001). Mean and median costs of medication for monotherapy (n = 507) were $8.25 and $6.23 and for polypharmacy (n =192) were $13.30 and $9.48,respectively (P = 0.001). The important variables for cost prediction of AM were duration of hospitalization, type of treatment, and type of psychiatric ward in the MLR model, and duration of hospitalization, type of diagnosed disorder, type of treatment, age, Chlorpromazine dosage, and duration of disorder in the NN model. Conclusion: Our findings showed that the artificial NN (ANN) model can be used as a flexible model for cost prediction of AM. [ABSTRACT FROM AUTHOR]

For drilling optimization, an equation for the rate of penetration (ROP) is necessary. The rate of penetration depends on many factors such as formation properties, mud properties, weight on the bit, rotary speed, mud hydraulics, and size/type of bit. Due to the difficulty of mathematical modeling of ROP, researchers have used experimental results or field data to develop a correlation for ROP. In this study, a new model based on artificial neural networks (ANNs) is designed that is able to predict the ROP using real field data gathered in an Iranian oilfield (Ahwaz oilfield). The new model was successful in predicting ROP. To obtain operating parameters that lead to maximum ROP, the corresponding mathematical equation of an ANN model was implemented in a procedure using a genetic algorithm, which is one of the most reliable methods of optimization, and at different depths the parameters leading to maximum ROP were obtained. This model and its results can be used in Pabdeh and Gurpi formations in all Iranian oilfields and similar shaly formations in the Middle East such as the Iraq, Jaddia, and Aaliji formations corresponding to the Pabdeh formation and the Shiranish formation corresponding to Gurpi. [ABSTRACT FROM AUTHOR]

Short-term load forecasting for small power systems, namely zonal STLF, is one of the issues in the load forecasting area which has vital value for electricity market competitors like retailers and ISO. This paper discusses the common problems related to short term load forecasting which are particularly raised in the small power systems. Identifying bad data which constitute a large portion of load data in these systems, choosing a proper manner for selecting ANN training data, and requirements which should be considered in the structure of zonal STLF to facilitate its employing to different power systems are investigated in this paper. Here, a comprehensive model which considers these problems as well as the generality of structure and the simplicity of implementation is proposed. This model is tested on Isfahan power system which is one of the regional power companies of Iran national power system. The results show that this structure can be implemented effectively and have an acceptable error in spite of large amount of bad data among historical data. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new bidding strategy for pay-as-bid market suppliers is introduced. This method is based on a systematic analysis of interactions of market with the suppliers via several market indices as well as forecasting important indices by artificial neural networks. Besides, the proposed method considers the practical limitations in the system and deals with incomplete information handling, closely. Next, a strategic bidding approach is proposed for optimal bidding by the suppliers. In these investigations, the paper focus is on the experimental situation of Iran electricity market as a pay-as-bid market and a sample generating company with several generating units from this market is considered as the benchmark. The results of applying this approach to this generating company are representative of good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran''s electricity market. The models’ input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran''s electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices. [Copyright &y& Elsevier]

Drought is a natural phenomenon that occurs in many places on the planet and may cause considerable damage. Selection of an integrated index for quantifying drought severity is a challenge for decision makers in developing water resources and operation management policies. In this study, the standardized precipitation index, water surface supply index, and Palmer drought severity index have been combined to develop an integrated index, called the hybrid drought index (HDI), using associated damage of drought events. Application of the HDI in drought severity prediction has been examined using two different types of artificial neural networks, namely, a probabilistic neural network and a multilayer perceptron network. These models have been selected due to their special characteristics that are suitable for prediction schemes. The proposed algorithm for developing HDI and drought prediction has been applied to the “Gavkhooni/Zayandeh-rud” basin in the central part of Iran. The results show the merits of each model in prediction of drought severity and model adaptation. The results also show the significant value of the proposed algorithm in formulation of a combined index for drought prediction. [ABSTRACT FROM AUTHOR]

Abstract: This research aims at improving the methods of prediction of hazardous geotechnical structures in the front of a tunnel face. We propose and showcase our methodology using a case study on a water supply system in Cheshmeh Roozieh, Iran. Geotechnical investigations had previously reported three measurements of the newly established method of TSP-203 (Tunnel Seismic Prediction) along 684m of the 3200m long tunnel up to a depth of 600m. We use the results of TSP-203 in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between TSP-203 results and those obtained by the methods of Rock Mass Rating classification (RMR – treated here as real values). Our results show that an appropriately trained neural network can reliably predict the weak geological zones in front of a tunnel face accurately. [Copyright &y& Elsevier]

• Introducing a new feature selection method based on entropy and mutual information to rank candidate inputs and eliminate redundant inputs according to their information value. • Using of wavelet transform to eliminate fluctuation behaviors of the electricity load and price time series decomposed the original signals. • Using of deep learning time series prediction based on LSTM to improve the forecast accuracy. Nowadays, a basic commodity for a human being to lead a standard lifestyle with human comfort irrespective of the nature of environmental conditions is electric power. The electricity load demand increases tremendously especially for a metropolitan city due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal device usage, etc. Hence, the accuracy of electricity load and its price forecasting is a deciding factor for the power distribution network to retain as an efficient, sustainable, and secure consumer-friendly network. On the other hand, based on the volatile, intermittent, and uncertain behavior of electricity load and price, an accurate, and robust forecast model should be designed. In this paper, a new hybrid forecast model for short-term electricity load and price prediction has been developed. The proposed method includes three modules: wavelet transform that is used to eliminate fluctuation behaviors of the electricity load and price time series, feature selection based on entropy and mutual information has been proposed to rank candidate inputs and eliminate redundant inputs according to their information value, and a new learning algorithm. The proposed learning method consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions. The performance of the proposed method has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Also, for further test, the load data in Iran have been used. [ABSTRACT FROM AUTHOR]

This paper examines some data mining techniques for lightning prediction. If we indicate by one the lightning event occurrence and by zero the non-occurrence of the event, then we will have a binary classification problem. In some cases, the dataset of lightning event is class imbalance. Thus, in the current research, the method of undersampling will be employed to generate several balanced datasets. Two binary classification algorithms, including neural networks and decision tree, were examined for lightning prediction. Furthermore, their performance was evaluated and compared. The proposed method was applied for some selected regions in Iran. Based on the evaluation results, decision tree outperforms feed-forward neural networks with one hidden layer for all datasets. • Lightning prediction considered as a binary classification problem. • Lightning dataset in this study is an imbalance dataset. • Under-bagging algorithm was applied for classification. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization. [ABSTRACT FROM AUTHOR]

Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6%) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.