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Ensemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events. [ABSTRACT FROM AUTHOR]

This paper targeted to investigate rehydration characteristics of dehydrated black beauty eggplant (which was dried under sun, by hot air, microwave, and infrared) at 25, 50, and 75°C, and to analyze rehydration ratio (RR) by artificial neural network (ANN) approach. RRs of both microwave‐processed specimens (between 5.66 and 7.91) and sun‐dried ones (between 5.38 and 6.37) were always greater than the others, at all rehydration temperatures. Rehydration temperature had an enhancing effect on RRs, and the image that closely resembled the fresh sample was captured in the microwave‐dried rehydrated samples. Nearly 180–240 min was adequate for rehydration of all slices. Zeroth‐ and first‐order kinetic models, Peleg, Peppas, and two‐term exponential decay models, as well as a new rational model were tested to describe rehydration kinetics. Two‐term equation was almost superior with high R2 (between 0.9734 and 0.9994) and the lowest root mean square error (RMSE) and χ2. Novel recommended mathematical expression was also successful (R2: 0.9679–0.9989, χ2: 0.0051–0.0975, RMSE: 0.0191–0.0866). Regarding relationship between actual and predicted RRs and performance indices of ANN equation, overall R and R2 were recorded as follows: 0.9975–0.9950 (sun‐dried) > 0.99642–0.9929 (hot air‐dried) > 0.9955–0.9911 (infrared‐dried) > 0.9907–0.9815 (microwave‐dried), respectively. The proposed ANN model and novel mathematical formula not only offer a considerable potential in predicting rehydration patterns and developing rehydration protocols in food production sector, but also contribute to save energy by completely understanding the process and optimizing conditions. Practical applications: The eggplant is a cherished vegetable often dried, providing a velvety texture and rich taste in Anatolian recipes. In Anatolia, colorful vegetables are strung together on strings, adorning traditional houses, and serving as a testament to the region's deep‐rooted cultural heritage. The Turkic people in Central Asia demonstrated remarkable skill in preserving plants by carefully drying leaves, stems, or roots. The process of rehydration entails the addition of water to dehydrated eggplant slices in order to restore their initial dimensions and attributes for being consumed. This investigation integrates the utilization of ANNs to simulate the rehydration procedures of eggplant. This simulation can be employed for the purpose of quality control and standardization within the food sector. Hence, food manufacturers can anticipate the rehydration characteristics of their products across various production batches and conditions, thereby guaranteeing adherence to quality benchmarks. [ABSTRACT FROM AUTHOR]

This study constructs a proposed model to investigate the link between environmental, social, and governance (ESG) disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability (XUSRD) index. In this context, this study considers 66 companies, examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron (MLP) artificial neural network algorithm. The relevant results are fourfold. (1) The MLP algorithm has explanatory power (i.e., R2) of 79% in estimating companies' ESG scores. (2) Common, environment, social, and governance pillars have respective weights of 21.04%, 44.87%, 30.34%, and 3.74% in total ESG scores. (3) The absolute and relative significance of each ESG reporting principle for companies' ESG scores varies. (4) According to absolute and relative significance, the most effective ESG principle is the common principle, followed by social and environmental principles, whereas governance principles have less significance. Overall, the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies' ESG scores; instead, companies should focus on the ESG principles that have the highest relative significance. The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index. [ABSTRACT FROM AUTHOR]

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model. [ABSTRACT FROM AUTHOR]

This study was carried out to predict the zooplankton density in the Cip reservoir (Elazığ) with an artificial neural network, using some water quality parameters. The plankton samples were collected monthly from Cip Reservoir in 2021- 2022, using a standard plankton net from three stations. Water temperature, dissolved oxygen, pH, electrical conductivity, secchi disk, alkalinity, total nitrogen and total phosphorus were measured. The actual values of zooplankton density and results obtained from the artificial neural networks were compared. Mean absolute percent error (MAPE) values were calculated with actual values and ANNs values. ANNs values were determined to be close to the real data. MAPE percentage value at the first station was determined as 1.143 for Rotifer, 0.118 for Cladocera, and 0.141 for Copepoda. The MAPE percentage value at the second station was determined as 0.941 for Rotifer, 0.377 for Cladocera, and 0.185 for Copepoda. The MAPE percentage value at the third station was determined as 0.342 for Rotifer, 0.557 for Cladocera, and 0.301 for Copepoda. In the present study, it has been seen that artificial neural networks with a learning feature are successful in predicting zooplankton densities in an aquatic environment. It can be concluded from the study that ANNs are a powerful tool for understanding their relationships with the environment [ABSTRACT FROM AUTHOR]

In recent years, with the advances in remote sensing and geospatial technology, various machine learning algorithms found applications in determining potentially flooded areas, which have an important place in basin planning and depend on various environmental parameters. This study uses ensemble models of decision trees (DT), gradient boosting trees (GBT), support vector machines (SVM) and artificial neural network (ANN) algorithms to generate flood susceptibility maps of the Eastern Mediterranean Basin located in the Eastern Türkiye where intense short-duration rainfall causes flash floods with devastating effects on the densely populated coastal region and agricultural areas. Results of test-set analyses showed that all algorithms were more successful with ensemble models included compared to the models alone. Among the ensemble models created, the ensemble ANN model substantially increased performed best when sued with training and test sets. It was observed from the flood susceptibility maps that the flood areas formed by ensemble models were more distributed than those created by a single machine learning algorithm, and with the help of ensemble models, the distribution of the parameters affecting the floods at the flood points more elucidating. Furthermore, the McNemar test was applied to assess the differences between the predictions of the generated models using test data. It is concluded, in general, that ensemble models created with the ANN and GBT algorithms can help decision-makers in identifying flood susceptibility areas. [ABSTRACT FROM AUTHOR]

The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study's short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey's SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered. [ABSTRACT FROM AUTHOR]

Heating degree-days (HDD) is a unit to measure the duration of cold weather during a 24-h period. Accurate HDD forecasting is crucial in terms of determining the energy needed for the heating of buildings. This work aims to introduce a model for one month-ahead forecasting of HDD time series data and their short-term future projection. Seasonal autoregressive integrated moving-average (SARIMA) and long short-term memory (LSTM) neural network models are utilized for this aim. Monthly HDD time series data from 2007 to 2021 in different climatic regions of Turkey are supplied by the Turkish State Meteorological Service (TSMS). Seasonally non-stationary HDD data for both models are converted to the position of stationary time series. Akaike information criterion (AIC) and Bayesian information criterion (BIC) values are utilized to optimize the models. The evaluation metrics of root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the forecast accuracy of the models. As a result, forecasts with approximate 95% confidence limits are made for the 60-month period (January 1, 2022 to December 31, 2026) from the proposed models. The results show that the LSTM and SARIMA models can provide future forecastings for HDD time series data with satisfactory precision. [ABSTRACT FROM AUTHOR]

Mix design of asphalt concrete is carried out in order to produce a mixture of asphalt bitumen and aggregate that satisfies both minimum design requirements and cost-effectiveness. Marshall mix design, similar to other mix design methods, aims to determine an optimum mix for a given design criteria, having an optimum bitumen content, and has still been one of the most used asphalt mix design method in the world. Marshall mix design procedure requires significant amount of time and skilled workmanship for lengthy laboratory experiments. Time required for the mix design may negatively affect construction schedule especially during the peak construction season. In Marshall mix design procedure, optimum bitumen content is determined based on Marshall stability and flow test results, and other volumetric properties of the mixes. Prediction models that quickly predict all required Marshall mix design parameters based on very few numbers of input parameters requiring significantly less amount of time to obtain compared to the time needed to obtain all Marshall mix design parameters through lengthy laboratory experiments could be quite useful. In this study, artificial neural network-based prediction models based on the data of 200 asphalt mixes designed for asphalt wearing course, obtained from 5th district of the General Directorate of Highways of Turkey, were developed to predict all required Marshall mix design parameters. As part of the paper, a Microsoft Excel Macro-based tool, to be potentially used by 5th district of the General Directorate of Highways of Turkey, was also developed that makes prediction of all required mix design parameters for any given mix in seconds using the developed models and very few numbers of user-defined input parameters requiring significantly less amount time to obtain so that optimum bitumen content could be quickly determined. This will significantly reduce the amount of time and other resources required to determine optimum bitumen content. [ABSTRACT FROM AUTHOR]

This study presents an effective framework that combines artificial neural network (ANN) and statistical methods to more efficiently, consistently, and reliably estimate the daily streamflow in ungauged basins. First, two statistical methods, including drainage area ratio (DAR) and standardization with mean (SM), are used to transfer hydrological data from gauged (donor) to ungauged (target) basins, which is known as the regionalization process. Second, to get better estimation performance, an ensemble approach is applied, which is mainly based on a weighted combination of DAR and SM. Finally, a successful strategy with an optimized ANN structure is built using daily areal precipitation for the target basin, the daily streamflow of the selected donor basin, and the estimated daily streamflow for the target basin from the best-fit method as model inputs. Its performance is tested in a case study from the Coruh River Basin, Turkey, that involved using datasets from seven streamflow gauging stations on the mainstream of Coruh River. The proposed approach has indicated the best performance on both training and testing sets. The proposed approach proves to be one of the best available practical solutions in the streamflow estimation for ungauged basins. [ABSTRACT FROM AUTHOR]

The accurate estimation of wind speed is crucial for wind energy production, given the exponential relationship between wind and power. However, this is a challenging task due to the stochastic nature of meteorology. In this study, the Weather Research and Forecasting Model (WRF) with a 9 km spatial resolution was used to simulate hourly wind speed values for Türkiye, using Global Data Assimilation System (GDAS) 0.25° boundary data. Several statistical metrics, including Root Mean Squared Error (RMSE), Mean Bias Error (MB), Index of Agreement (IoA), and Pearson correlation, were used to evaluate the performance of the WRF model. The WRF model, which used CONUS parametrization and was supplied with GDAS boundary data every 6 h, operated for 17,520 h in a 1-month consecutive run. The ANN model, which has Hecht-Nielsen (2n + 1) topology, was used to perform hindcasting of the WRF model. The input layer of the ANN model used temperature, pressure, and wind speed values obtained from WRF. The analysis was done spatio-temporally for 2 years and presented with seasonal and annual performance values. After applying the ANN model to the WRF model, which had initial values of MB of 1.42, RMSE of 2.26, R of 0.51, and IOA value of 0.02, the new MB, RMSE, R, and IOA values were found to be 0.04, 0.96, 0.56, and 0.60, respectively. Therefore, it can be concluded that the ANN model improved the WRF model's wind speed prediction performance in Türkiye by 11% on average, relatively. [ABSTRACT FROM AUTHOR]

By definition, an earthquake is a naturally occurring event. This natural event may be a disaster that causes significant damage, loss of life, and other economic effects. The possibility of predicting a natural event such as an earthquake will minimize the negative effects mentioned above. In this study, data collection, processing, and data evaluation regarding earthquakes were carried out. Earthquake forecasting was performed using the RNN (recurrent neural network) method. This study was carried out using seismic data with a magnitude of 3.0 and above of the Düzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of earthquakes were calculated. The detection of earthquakes within a specific time interval in the Marmara region of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the generation of predictions for the future highlight the importance of this study. Our results demonstrated that the prediction performance could be significantly improved by incorporating the b and d coefficients of earthquakes, as well as the data regarding the distance between the Moon and the Earth, along with the use of recurrent neural networks (RNNs). [ABSTRACT FROM AUTHOR]

Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential that an understanding of hazelnuts' aerodynamic properties, i.e., terminal velocity and drag coefficient, is acquired. In this study, the moisture, mass, density, projected area, surface area, and geometric diameter were used as independent variables in the data set, and the dependent variables terminal velocity and drag coefficient estimation were determined. In this study, logistic regression (LR), support vector regression (SVR), and artificial neural networks (ANNs) were used based on machine learning methods. When the results were evaluated according to R2 (determination coefficient), MSE (mean squared error), and MAE (mean absolute error) metrics, it was seen that the most successful models were the ANN, SVR, and LR, respectively. According to the R2 metric, the ANN method achieved 91.5% for the terminal velocity of hazelnuts and 85.9% for the drag coefficient of hazelnuts. Using the independent variables in the study, it was seen that the terminal velocity and drag coefficient value of hazelnuts could be successfully estimated. [ABSTRACT FROM AUTHOR]

Heat index (HI) is a temperature that the human body feels or perceives, as opposed to the physical air temperature measured by a thermometer. The goal of this study was to create a monthly average HI map in the external environment for Türkiye using a mathematical model developed by AccuWeather, an artificial neural network (ANN), and an adaptive neuro-fuzzy inference system (ANFIS) approach. In creating Türkiye's HI map, measurable parameters such as hourly dry bulb temperature, relative humidity, wind speed, and atmospheric pressure data from 81 measuring stations were used. According to the simulations, due to the lack of measurable data, HI, which cannot be computed in each location, can be efficiently predicted using geographical inputs to ANN and ANFIS methods. The outcomes demonstrated that predicted HI values with the developed ANN and ANFIS models are in good agreement with the actual HI calculated values using the AccuWeather method for all cities, but the accuracy of the machine learning models varies depending on the city's measured data. Although MAE and RMSE values for generated ANN and ANFIS machine learning models are within acceptable ranges, ANN outperforms ANFIS for all cities tested during the estimation of HI values. ANN and ANFIS models are capable of correctly predicting HI values when the month of the year, latitude, longitude, and altitude values are provided. This eliminates the need for excessive testing and saves time, labor, and financial resources. [ABSTRACT FROM AUTHOR]

Earthquakes are natural events that affect the spatial and temporal dynamics of land use/land cover. Here, we have focused on the city of Duzce, NW of Turkey, which has experienced the M > 7 doublet earthquakes in 1999. In order to conduct land cover change analysis and future projection, we have utilized integrated the multilayer perceptron neural network-based Markov chain model combined with geographical information systems. Our results show that most of the land use in 1990 was 77,601 hectares (65.89% of the region) consist of forest and shrubs. By 2020, agricultural land was the most expanding land class in the region with 48.80%. The 1999 earthquake caused the city to grow horizontally in the long run, and this expansion has ended the occupation in grade-one-agricultural lands that prevailed in the region. This horizontal development also caused the residential areas that covered 2.76% of the study area in 1999 expand to the ratio of 20.37% in 2050. It has been determined that the most effective driver in these changes in the land cover is the distance from urban with a Cramer's value of 0.4299. For this reason, the area that is most exposed to land cover change is the forest and shrub areas occupied by the city, which has spread to the northeast due to the earthquake. It is concluded that the earthquakes can change the land cover of the city significantly in various aspects in the long term, contrary to the approved zoning plans. [ABSTRACT FROM AUTHOR]

As part of this research, the Ladik-Sarayönü area of Konya province's air quality has been assessed utilizing an AI (Artificial Intelligence) method. A total of 103 field samples were analyzed experimentally. Data from experiments was used to inform the design of a multi-layer perceptron feed-forward back-propagation artificial neural network model. The Bayesian method has been employed as the training procedure in an artificial neural network model with 15 neurons in its hidden layer. One hundred experimental data points were used to develop a network model that predicts mercury values of the geoaccumulation index value in the output layer based on the following input variables: mercury, distance to the pollution source, source of pollution, characteristics of the sampled place and the primary factor that controls moving parameters. The majority (90%) of the data is used for the model's training process, while the remaining (10%) is used for validation. By comparing the model's anticipated outcomes with experimental data, an artificial neural network was used to evaluate the model's prediction performance. To forecast mercury values of the geoaccumulation index, the created artificial neural network had an error rate of − 4.04 to 3.98% (with an average of − 0.58%). The MSE for the network model is 2.1 × 10−1, and the R value is 0.9533. [ABSTRACT FROM AUTHOR]

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (ϒVH, ϒVV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ϒVH, ϒVV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies. [ABSTRACT FROM AUTHOR]

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0–20 and 20–40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4–5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2–3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution. [ABSTRACT FROM AUTHOR]

Meeting customer needs in a timely manner has a significant impact on customer satisfaction. For this reason, the planning process has successfully influenced the success of sales activities. The crucial point for the success of the planning process depends on the sales forecasts. Sales forecasting estimates the quantity required by the customer needs. It helps in determining sales targets as campaigns, pricing, brand and product communication, and distribution channels are incorporated in the sales forecast. In this paper, we use regression and artificial neural networks to predict automobile sales in Turkey. The performance of regression is compared with that of an artificial neural network, and it is shown which network is able to predict. Thus, the result of the study, automobile sales in Turkey, was predicted and compared with the actual sales for 2020. The result is that the best prediction method will determine the automobile sales in Turkey. [ABSTRACT FROM AUTHOR]

In this study, marketing live weights (120th day) were predicted using artificial neural network model according to the herd, gender, birth type, maternal age, birth weight, body weight at 60th day and weaning weight (90th day) measurements of 12983 hair goat kids born between 2018-2021 years. Artificial neural networks (ANN) have been frequently used as an alternative to classical regression analysis in recent years, especially in future estimation studies in the field of livestock, and also in many different fields. In this study, it was aimed to predict the marketing weights of hair goats according to the holding, gender, birth type, maternal age, birth, 60th day and weaning weights with the ANN model. For this purpose, the multi-layer feed-forward backpropagation algorithm the ANN model, in which the number of hidden layers is one and the numbers of hidden neurons are three, was used. This model performance metrics were obtained for training set as 0.98, 0.62 and 0.55; for validation set as 0.97, 0.62 and 0.55, respectively. According to these results, it was determined that ANN can be used successfully in terms of estimation of marketing live weight in Hair goat kids. Estimating the marketing weight will enable the economic cost calculations to be obtained from kids to be evaluated both based on Turkey and on the farm basis, and to reveal future projections. [ABSTRACT FROM AUTHOR]

The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular, estimating the patient numbers and achieving an informed management of the dilemma are crucial in terms of the strategic decisions to be taken. The Covid-19 pandemic presents an important case in this context. Sustaining the efforts to cope with and to put an end to this pandemic requires investigation of the spread and infection mechanisms of the disease, and the factors which facilitate its spread. Covid-19 symptoms culminating in respiratory failure are known to cause death. Since air quality is one of the most significant factors in the progression of lung and respiratory diseases, it is aimed to estimate the number of Covid-19 patients corresponding to the pollutant parameters (PM10, PM2.5, SO2, NOX, NO2, CO, O3) after determining the relationship between air pollutant parameters and Covid-19 patient numbers in Turkey. For this purpose, artificial neural network was used to estimate the number of Covid-19 patients corresponding to air pollutant parameters in Turkey. To obtain highest accuracy levels in terms of network architecture structure, various network structures were tested. The optimal performance level was developed with 15 neurons combined with one hidden layer, which achieved a network performance level as high as 0.97342. It was concluded that Covid-19 disease is affected from air pollutant parameters and the number of patients can be estimated depending on these parameters by this study. Since it is known that the struggle against the pandemic should be handled in all aspects, the result of the study will contribute to the establishment of environmental decisions and precautions. [ABSTRACT FROM AUTHOR]

Supply-demand management of the energy sector is of prime importance for rapidly growing economies and developing countries. Depending on the increasing population, the rising economy, and the developments in the industry, the countries' energy needs are increasing to a great extent. Therefore, using models to accurately and reliably predict future electricity supply-demand trends has attracted the attention of consumers and investors in this field. In this study, Artificial Neural Networks, Ridge Regression, Lasso Regression and Support Vector Regression, proven successes in the literature, are used to realize Turkey's short-term electric load demand estimation. Data used in the forecasting models were obtained from the Turkish Electricity Transmission Corporation. A one-hour future estimation is accomplished using a past year-long dataset of electrical energy. To compare the results obtained from the methods, RMSE, MAE and R2 values, frequently used in the literature, were calculated. The comparison results show that the Artificial Neural Networks were more successful with RMSE=0.86, MAE=0.62 and R2=0.97 results among the developed machine learning models. [ABSTRACT FROM AUTHOR]

It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and Mwl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R²) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and Mwl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey. [ABSTRACT FROM AUTHOR]

The Büyük Menderes watershed is the largest drainage watershed in Western Anatolia with an area of approximately 26,000 km2. In the study area, almost 863 landslides occurred, extending over 222 km2 with a mean landslide area of 0.21 km2. In this study, landslide susceptibility assessments were carried out using artificial neural network method, which is one of the data-driven methods. In this study, that will contribute to the mitigation or control of the landslides caused by the reasons controlling the spatial and temporal distribution of landslides created in the GIS and MATLAB environment by using scientific and technological approaches within the framework. Since derivative activation function is also used in back-propagation artificial neural networks, its derivative is easily calculated in order not to slow down the calculation. Levenberg–Marquardt back-propagation (LM), resilient back propagation back-propagation (trainrp), scaled conjugate gradient back-propagation (trainscg), conjugate gradient with Powell/Beale restarts back-propagation (traincgb), and Fletcher-Powell conjugate gradient back-propagation (traincgf) algorithms are used, which constantly interrogate the link between the input parameter and the result output, and at least one cell's output is given as an input to any other cell. Geology, digital elevation model, slope, topographic wetness index, roughness index, plan, profile curvatures, and proximity to active faults and rivers were used as landslide conditioning factors. In susceptibility assessments, landslides were separated by 70% analysis, 15% test, and 15% validation datasets by random selection method. The performances of the landslide susceptibility maps were assessed by the area under the ROC curve (AUC), accuracy (ACC), precision, recall, F1 score, Kappa test error histogram, and confusion matrix, respectively. The area under the receiver operating characteristic curves, analysis, testing, validation, landslides, and study areas were found between 0.873 and 0.911. The susceptibility map had a high prediction rate in which high and very high susceptible zones corresponded to 26% of the study area including 82% of the recorded landslides. [ABSTRACT FROM AUTHOR]

The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. [ABSTRACT FROM AUTHOR]

Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels. [ABSTRACT FROM AUTHOR]

Short-range predictions of crop yield provide valuable insights for agricultural resource management and likely economic impacts associated with low yield. Such predictions are difficult to achieve in regions that lack extensive observational records. Herein, we demonstrate how a number of basic or readily available input data can be used to train an Artificial Neural Network (ANN) model to provide months-ahead predictions of cotton yield for a case study in Menemen Plain, Turkey. We use limited reported yield (13 years) along cumulative precipitation, cumulative heat units, two meteorologically-based drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)), and three remotely-sensed vegetation indices (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI)) as ANN inputs. Results indicate that, when EVI is combined with the preceding 12-month SPEI, it has better sensitivity to cotton yield than other indicators. The ANN model predicted cotton yield four months before harvest with R2 > 0.80, showing potential as a yield prediction tool. We discuss the effects of different combinations of input data (explanatory variables), dataset size, and selection of training data to inform future applications of ANN for early prediction of cotton yield in data-scarce regions. [ABSTRACT FROM AUTHOR]

Precise electricity demand forecasting has principal significance in the energy production planning of the developing countries. Especially during the last decade, numerous recent methods have been utilized to predict the forthcoming electricity demand in different time resolutions accurately. This contribution presents a novel approach, which improves the forecasting of Turkey’s electricity demand in monthly time resolution. An artificial neural network model has been proposed with appropriate input features. Yearly-based gross demand shows approximately linear increment due to population increase and economic growth, while monthly-based gross demand indicates an oscillation due to the effect of seasonal temperature fluctuations. However, there is no clear linear relation between electricity demand and temperature; for the ideal case, it is the V-shaped curve around balance point temperature. Since temperature levels in each region of the country demonstrate a high variance even in the same time period, weighted average temperature point was calculated with respect to the population weights of the selected regions of Turkey. In order to fit a function for monthly oscillations, a linear function according to weighted average temperature point was created. Unemployment data was added to the training data set as an indicator of economic fluctuations. The mean absolute percentage errors of the model were calculated for training, validation, and testing as 3.77 %, 2.02 %, and 1.95 % respectively. [ABSTRACT FROM AUTHOR]

Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4 -2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777. [ABSTRACT FROM AUTHOR]

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance. [ABSTRACT FROM AUTHOR]

Uniaxial compressive strength (UCS) of rocks is the most commonly used parameter in geo-engineering application. However, this parameter is hard for measurement due to a time consuming and requires expensive equipment. Therefore, obtaining this value indirectly using non-destructive testing methods has been a frequently preferred method for a long time. In order to obtain multiple regression models, input parameters need many assumptions. Thus, the estimation of the mechanical properties of rocks using by machine learning methods has been investigated. In this study, UCS values of rocks were estimated by reformulating with artificial intelligence-based age-layered population structure genetic programming (ALPS-GP) which is one of machine learning methods. Artificial neural network (ANN) and ALPS-GP models were performed to predict UCS from porosity, Schmidt hammer hardness and ultrasonic wave velocity test methods. For this purpose, the mentioned three tests (porosity, Schmidt hammer hardness and P-wave velocity) were carried out on ten different stones from Turkey. ANN was performed to evaluate this new technique. Reliability of UCS values determined by models was checked with mean absolute error (MAE), coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) values. These values were calculated as 1.64, 0.98, 2.11 and 99.81 for ANN, and 2.11, 0.98, 2.50 and 97.86 for ALPS-GP, respectively. It was observed that both methods used were quite successful in UCS estimation. The most important advantage of the ALPS-GP model is providing an equation for UCS estimation. In the light of the obtained findings, it has been revealed that this equation derived from ALPS-GP can be used in UCS estimation processes of similar rock types (limestone, dolomite and onyx). [ABSTRACT FROM AUTHOR]

Energy demands of countries are changing rapidly in parallel with development, industrialization, urbanization, the spread of technology, prosperity, and population growth. Energy use in the transport sector in the last few years, Turkey has shown a significant increase. Therefore, energy management and predicting are critically important to environmental safety and the upcoming economic well-being. In recent years, studies to determine the energy demand have accelerated. In Addition, in order to estimate the demand levels in the most accurate way, the most appropriate model should be selected. In this study, different models for predicting Turkey's transport energy demand by using artificial neural networks have been established. Population, oil prices, gross domestic product, ton-km, vehicle-km, and passenger-km are selected as parameters by considering 1975 and 2016 data. The best model is tried to be obtained with the models in which different parameters are used together. The best model was established with the oil price, population, ton-km and it was determined that this model had the lowest error and highest R² values. [ABSTRACT FROM AUTHOR]

Since global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy. The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network's hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 5.8262x107 W/m2 for the testing data set. The ANN's testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient. [ABSTRACT FROM AUTHOR]

The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. In this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (Eti, GPa) and tangential Poisson's Ratio (vtl) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), NW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, do, and sorting coefficient, Sc) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the Eti and vti. The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. It was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones. [ABSTRACT FROM AUTHOR]

Summary: As a consequence of the growing world population along with the rapid developments in technology, electric energy consumption is increasing. Considering the rate of electricity consumption, investment in electric energy generation continues to rapidly expand worldwide. In addition, because of increasing electric energy consumption, the problem of ensuring supply security is an issue that should be considered by all countries. As a result of this issue, it has become necessary to predict short‐term, mid‐term, and long‐term electric energy consumption rates in order to plan for future generation investments. In this study, a feedforward neural network (FFNN) model based on Manta Ray Foraging Optimizer algorithm was proposed to forecast the electric energy consumption rates of Bursa, an industrial city in Turkey, with a rapidly growing economy. The dataset for the proposed model consists of the average data for environmental conditions, the days of the week, and the electric energy consumption rates. Using this dataset, simulation trials were conducted to find the optimal values of weight and bias coefficients in different network structures. The simulation results obtained from the proposed approach were compared with the results from the neural network models trained using the Hierarchical Particle Swarm Optimizer with Time Varying Acceleration Coefficients, improved grey wolf optimization, gradient‐based optimizer, Symbiotic Organisms Search (SOS), Harris Hawks Optimization, Spotted Hyena Optimizer, Salp Swarm Algorithm, and Arithmetic Optimization Algorithm. In order to test the success of the proposed model, the results of both the training and the testing process were analyzed according to the mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria. In addition, the proposed approach was tested using five classification problems of varying difficulty levels presented in the literature in recent years. The simulation results were evaluated statistically and compared to the results of the other algorithms. According to the simulation results from both datasets, in the five classification problems and in the prediction of electric energy consumption, the neural network model trained with the MRFO algorithm performed better than those trained with the other algorithms. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rize district is an important tea production site in Turkey, which is known for high quality tea. Determining the temporal changes is very crucial from the viewpoint of agricultural management and protection of tea areas. In addition, delineation of tea gardens using photogrammetric evaluation techniques for a single orthoimage takes approximately 8 h of labour work, which is both costly and time-consuming process. To overcome these issues, a method is proposed for demarcation of tea gardens from high-resolution orthoimages. In this article, a hierarchical object-based segmentation using mean-shift (MS) and supervised machine learning (ML) methods are investigated for delineation of tea gardens. First, the MS algorithm was applied to partition the images into homogeneous segments (objects) and then from each segment, various spectral, spatial and textural features were extracted. Finally, four most widely used supervised ML classifiers, support vector machine (SVM), artificial neural network (ANN), random forest (RF), and decision trees (DTs), were selected for classification of objects into tea gardens and other types of trees. Photogrammetrically evaluated tea garden borders were taken as reference data to evaluate the performance of the proposed methods. The experiments showed that all selected supervised classifiers were effective for delineation of the tea gardens from high-resolution images. [ABSTRACT FROM AUTHOR]

The efficient operation of power systems and future planning, electricity load forecast is very important. Load estimation is based on predicting future electric load by examining past conditions. Short-term load prediction plays a decisive role in the load sharing of power plants. It also allows to overcome shortcomings caused by sudden load increases and power plant losses. Weather conditions are effective in short-term electrical load estimation. Daily or hourly electricity consumption data is generally used for short-term load estimation. In this study, daily electrical energy consumption of Turkey in the four years of data were used. Short-term load prediction modeling has been carried out. In this modeling, past electrical load values and temperature values were used as input, and in order to increase the prediction accuracy, the characteristics of the days were categorized weekly and classified according to the seasons. Different Artificial Neural Network models have been created according to input data, weekly categorization, and season criteria. In the study, mean absolute percentage error values were calculated. Among the models developed with ANN, the best MAPE value was 2.51% and the worst MAPE value was 4.48%. When the season criterion is added, the MAPE value is more successful. [ABSTRACT FROM AUTHOR]

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers' short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper. [ABSTRACT FROM AUTHOR]

One of the biggest problems of today is environmental pollutants and their effects. When the sources of environmental pollutants are examined, many sectors are under examination. One of these sectors is the transportation sector with 19% emission share in all sectors. When the modes of the transportation sector are examined, the most polluting is road transportation. Therefore, the general policy of the countries is to focus on rail and maritime transport, which saves fuel consumption in both freight and passenger transport. Along with saving in fuel consumption, CO2 emissions also decrease. In this study, change of railway transport mode share are examined for Turkey and prediction models are created by using artificial neural networks (ANN) method for rail transport CO2 emission. In the prediction models, six variables are examined: length of railways, the number of passengers, freight amount, train-kilometer, passenger-kilometer and ton-kilometer for railway transportation. The models are created according to the combinations of input variables with 2, 3, 4, 5 and 6 combinations. New inputs are created by using principal component analysis (PCA) in order to both increase performance and predict the effects of the six variables used in the model. As a result of the performance evaluation of the models, the best prediction model is NPFA which have 2 variables. Mean Square Error (MSE), Mean percentage error (MPE) and coefficient of correlation (R) performance values of the model are 6.48 × 10-5, 0.125% and 99.65%, respectively. In addition, PCA has significant effects on models. [ABSTRACT FROM AUTHOR]

The excavation of rock masses has a crucial importance for the safety and cost of construction of engineering projects. In this study, a flexible excavation assessment (EXCASS) system was developed with two components, the geological strength index (GSI) and the point load strength index (Is50). In order to develop the system, artificial neural network (ANN) analysis were performed using datasets from surface excavations (12 sites in Turkey and 61 sites in Greece reported by Tsiambaos and Saroglou (2010)). The EXCASS system in this study includes two parameters, namely the excavation power index (EPI) and the excavation performance rating (EPR) ranging between the value of 0 and 100. The optimum excavation power index (EPIopt) can be determined theoretically by using ( GSI 2 × Is 50 ) as an input when EPR is equal to 0, for the selection of the optimum excavation method. EPR is a rating which provides relative information on the ease or difficulty of the excavation method. Although the value of EPR varies between 0 and ± 100 in the EXCASS system, the use of the excavation method for "normally classes" is recommended in order to achieve costly and efficient excavation. The EXCASS system, on the other hand, has an ability to respond to possible technological developments by associating the excavation method with the EPI rating. [ABSTRACT FROM AUTHOR]

This article proposes and tests a model for the interaction effect of the organizational safety climate and behaviors on workplace injuries. Using artificial neural network and survey data from 306 metal casting industry employees in central Anatolia, we found that an organizational safety climate mitigates workplace injuries, and safety behaviors enforce the strength of the negative impact of the safety climate on workplace injuries. The results suggest a complex relationship between the organizational safety climate, safety behavior and workplace injuries. Theoretical and practical implications are discussed in light of decreasing workplace injuries in the Anatolian metal casting industry. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to compare the predictions of the academic achievement of the artificial neural networks (ANN) run in MATLAB and SPSS software and to determine the factors related to their academic achievement. Sample consisted of 465 students who were studying at Grade 4 in primary schools in the Central Anatolian Region of Turkey in 2017. A 12-questions questionnaire was used as the collection tool. For the content validity of the questionnaire, expert opinions were received. The KR20 reliability coefficient was calculated as .60. An exploratory factor analysis was run for the construct validity. In the ANN model, the items related to the academic achievement in the questionnaire were considered as independent variables / inputs, and the academic achievements of the previous year as the dependent variables / outputs. The predictions of the academic achievement of the ANN models were analyzed in MATLAB R2013a and SPSS 24.0 software and the regression coefficients of the independent variables were examined. It was found that MATLAB software had a higher rate of the correct prediction compared to SPSS. In the regression coefficients of the independent variables, some differences and similarities between the results of MATLAB and SPSS were found. [ABSTRACT FROM AUTHOR]

Summary: Despite the impact of personnel scheduling on the sustainability of production processes, it is a rarely studied problem in the electricity generation sector, whose main purpose is sustainable energy generation. From this point of view, personnel scheduling problem is handled in this study based on the impact of this problem, which focuses on fair and balanced job distribution according to the personnel qualifications, on the sustainable generation in the power plants. With its advantages such as ease of operation and maintenance, fast commissioning, lower greenhouse gas emissions compared to other fossil fuels and etc. one of the biggest Natural Gas Combined Cycle Power Plant (NGCCPP) in Turkey is chosen as application area. The main purpose of the study is to demonstrate the effect of fair, balanced and competency‐based personnel scheduling on generation stoppages resulting from personnel planning in power plants, which have great importance in energy supply security of the countries. This study is the first in the literature in terms of three different aspects. Firstly, in the energy sector, it is different in that it is a personnel scheduling study that takes into consideration the job assignment of the personnel according to their abilities and minimizes costs. Secondly, the large size of the problem is different from that of previous studies in the literature. Finally, the number of personnel employed in this generation facility varies according to the season. At this point, the past generation data are analyzed using an Artificial Neural Network (ANN) method and an estimation is made, and personnel scheduling is performed in the light of this information. Consequently, the proposed multi‐objective mathematical model supported with multi‐criteria decision making and ANN, shutdown rate of the power plant due to operator error is reduced 67.3%, and personnel satisfaction level is increased from 42% to 89%as solution. [ABSTRACT FROM AUTHOR]

Air pollution was predicted in this study by using multiple linear regression and 42 different artificial neural network models in Iğdır/Turkey. Daily air quality data for the years 2016–2018 have been used in the modeling. In the prediction of the particulate matter which has 10 μm or less in diameter (PM10) concentration, sulfur dioxide, nitrogen oxides, nitrogen monoxide, ozone, nitrogen dioxide, relative humidity, air pressure, wind direction and wind speed data were used as input parameters. In the artificial neural network structures, two different learning functions, three different transfer functions and seven different neuron numbers were examined in the MATLAB software. According to results, multiple linear regression did not predict the PM10 concentration in the atmosphere. The R2 value was determined as 0.543 for the multiple linear regression. In this model, the RMSE, MAE and R2 were determined as 0.0488, 0.0248 and 0.9826, respectively. Since the R2 value in this model was quite high, it was concluded that the model is suitable for the prediction of PM10 concentration. [ABSTRACT FROM AUTHOR]

Copyright of Hacettepe University Journal of Economics & Administrative Sciences / Hacettepe Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Hacettepe University, Faculty of Economic & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: In this study, artificial intelligence models that identify sunn pest‐damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG and 150 SDG being used for classification. Thanks to the constructed imaging setup, photos of the 300 wheat grains are obtained. Seventeen visual features of each wheat grain are extracted by image‐processing techniques and evaluated in three different groups of dimension, texture and pattern as visual parameters. Artificial bee colony (ABC) optimization‐based artificial neural network (ANN) and extreme learning machine (ELM) algorithms are implemented to classify the damaged wheat grains. RESULTS: A correlation‐based feature selection (CFS) technique is also utilized to find the most effective among the 17 features. In the classification process using five selected features, the mean absolute error (MAE) and root mean square error (RMSE) values for ABC‐based ANN are calculated as 0.00174 and 0.00433 respectively. The proposed technique is integrated into graphical user interface (GUI) software to construct an effective detection system for practical use. CONCLUSION: The results indicate that, thanks to the modified ANN algorithm and implemented CFS algorithm, the detection accuracy of damaged wheat grains is considerably increased. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Energy efficiency initiatives are now more noteworthy due to awareness and sensitivity in the use of resources in rational, optimal and effective ways. The uncertain and dynamic structure of the electricity distribution market requires continuous improvement and efficiency activities/strategic decisions by adding new investments. Energy efficiency assessment plays an important role in improving energy efficiency. In this study, Data Envelopment Analysis (DEA) was employed to investigate the efficiency performance of twenty-one electricity distribution companies in Turkey. The results of DEA revealed that seven of the twenty-one electricity distribution companies were efficiently attempted in Turkey. After utilizing the DEA model, an Artificial Neural Network (ANN) method based on DEA was constructed, and then the efficiency of each company was predicted. According to the proposed integrated model, with incorporating new/alternative electricity companies, investment plans can be easily evaluated from a real perspective, and their performances can be predicted accurately. The study is expected to assist direct energy decision-makers and investors and help them in their investment plans. [ABSTRACT FROM AUTHOR]

The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. [ABSTRACT FROM AUTHOR]

Electricity is one of the most important end-user energy types in today's world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies. [ABSTRACT FROM AUTHOR]