Search

The smarter world needs more efforts to purposeful manage and usage of technologies, science, artificial intelligence, and artificial neural networks, as their product. One of the main tools to facilitate collecting beneficial information is reviewing the publications of the target domain. To this end, the present paper attempts to categorize recent trends and innovations in the application of artificial neural networks in water quality and pollution research. The most prevalent methods for water pollution and quality prediction modeling during 2011–2020 were BPNN and normal MLP.. Conducting and comparing other analytical methods such as ANN, RBFNN, LSTM, and CNN to find accurate results are in the next orders. Moreover, along with the emerging and development of the internet of things (IoT), it was observed that IoT as a real-time technology for collecting relevant data, which appeared in the water quality analysis since 2016–2017, was used increasingly in 2019 and 2020. Furthermore, reviewed papers revealed that pH, chemical oxygen demand, and biochemical oxygen demand were the most water quality indicators. Regionally, drivers in China were the most investigated sites during the last decade, and then India and Turkey were the second and third place. [ABSTRACT FROM AUTHOR]

This paper introduces machine learning-based Turkiye-specific ground motion models for the geometric mean horizontal component of peak ground velocity (PGV). PGV is a significant intensity metric to measure and diagnose potential earthquake damage in structures. Reliable prediction of PGV is of essential importance in precise calculations of seismic hazard. The efficiencies, reliabilities, and capabilities of various machine learning algorithms, including Random Forest, Support Vector Machine, Linear Regression, Artificial Neural Network, Gradient Boosting, and Bayesian Ridge Regression, are evaluated and compared. The most recently compiled Turkish strong motion database, which consists of over 950 earthquakes occurring from 1983 to 2023, is used for shaping the models' ability to learn and make accurate predictions. Three feature selection methods- Least Absolute Shrinkage and Selection Operator, Recursive Feature Elimination, and Pearson's Correlation- representing embedded, wrapper, and filter approaches, respectively, are applied to determine the most suitable estimator parameters to predict PGV. Residual analyses and statistical evaluation metrics are employed to measure the performance and effectiveness of the machine learning models. Among the algorithms applied, Gradient Boosting demonstrates exceptional success in predicting PGV, particularly when utilizing all estimator parameters (features) collectively. The Gradient Boosting model exhibits superior predictive capabilities compared to existing ground motion models. It is applicable to shallow crustal strike-slip and normal faulting earthquakes with moment magnitude ranging from 3.5 to 7.8 and Joyner and Boore distance up to 200 km. [ABSTRACT FROM AUTHOR]

Copyright of Natural Resources & Technology is the property of Goce Delcev University, Natural Resources & Technology 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.)

Artificial intelligence (AI) has been widely affecting our lives in many ways for the last ten years. Potential applications of AI are currently being used in many sectors. However, the usage of AI has been very limited in sports science compared to the other sectors. In this study, we developed an artificial neural network model to estimate the market values of the players in the Turkish Super Football League. While the market value was selected as an output, the input values were: minutes played; goals scored; xG: assists; xA; defensive duels won %; tackle success %; shots on target %; Short-middle pass accuracy %; long pass accuracy %; and accuracy of passes to penalty area. After creating a neural network based on the input-output values with high training, validation and testing statistical values, input values were computed with the neural network created and then the output values were estimated. In conclusion, an artificial neural network is becoming one of the important modeling methods in all areas of life. Although the application of artificial neural networks is very limited in sports science, it is one of the suitable science disciplines where there is a lot of statistical data. The methodology proposed in this paper can also be used for talent selection in football. Moreover, it may help stop criticism by TV sports programmes and newspapers because market value estimation is based on fair performance parameters. Further studies are strongly recommended, not only for football but also for other sports disciplines. [ABSTRACT FROM AUTHOR]

Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important; however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers. [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]

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]

Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or expert‐based methods. For this reason, the solutions backed by Machine Learning (ML) algorithms are key to improve the detection rates of such rare cases. In this paper, we investigate the application of ML in the healthcare domain for the detection of rare thyroid nodules. The utilized dataset is collected from 636 distinct patients in 99 unique days in Turkey. In addition to the texture feature data of the Ultrasound (US), we have also included the scores of different assessment methods created by different health institutions (e.g., Korean, American and European thyroid societies) as additional features. For detection of extremely rare malign cases, we use auto‐encoder based neural network model. Through numerical results, it is shown that the auto‐encoder based model can result in an average Recall score of 0.98 and a Sensitivity score of 1.00 for detecting malign and non‐malign cases from the healthcare dataset outperforming the traditional classification algorithms that are trained after Synthetic Minority Oversampling Technique (SMOTE) oversampling. [ABSTRACT FROM AUTHOR]

Patient arrivals at the emergency department (ED) of hospitals has an unpredictable behavior. So that, adequate forecasting of this process can serve a management baseline to better allocate ED human resources and medical equipment. In this paper, a multi-method patient arrival forecasting outline for EDs is developed. The methods followed within this study include single methods as linear regression (LR), autoregressive integrated moving average (ARIMA), artificial neural network (ANN), exponential smoothing and hybrid methods as ARIMA–ANN and ARIMA-LR. As the subject of the study, a private hospital ED case in Turkey is carried out. Data of ED patient arrivals for the year of 2016 was used to set up models. Forecasting performance of the multi-method outline was measured using mean absolute percentage error. The ARIMA–ANN hybrid model is shown to outperform in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying these methods to model ED patient arrivals and making an overall assessment among them. The results can be used to aid in strategic decision-making on ED staffing and scheduling policy planning in response to predictable arrival variations. [ABSTRACT FROM AUTHOR]

Wind is a stochastic and intermittent renewable energy source. Due to its nature, it is extremely hard to forecast wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO2 and other greenhouse gases (GHG) which are harmful to the environment will not be released into the atmosphere, while generating electrical energy. This paper presents a novel precise, fast and powerful hybrid metaheuristic wind power forecasting approach based on statistical and mathematical data from real weather stations. The model was developed as a hybrid metaheuristic algorithm based on artificial neural networks (ANNs), particle swarm optimization (PSO) and radial movement optimization (RMO). Real-time wind data was gathered from wind measuring stations (WMS) at two separate places in Burdur and Osmaniye cities, Turkey. The key contribution of this new model is the ability to perform wind power forecasting studies, without needing wind speed data, with high accuracy and rapid solutions. Also, wind power forecasting studies with high accuracy have been carried out despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61 m and 60.3 m using temperature (3 m), humidity (3 m) and pressure (3.5 m) data. The performance results were presented in tables and graphs. [ABSTRACT FROM AUTHOR]

This paper looks into the Greek-Turkish arms race a decade after an earlier contribution to the issue that relied heavily on artificial neural networks. The time period between the two papers contributes to the reliability of the results derived, not just by increasing the number of observations, but also mainly by incorporating the progress made in the realm of artificial intelligence. The focus on the case of both countries unlike the paper mentioned above that dealt with just the Greek side provides ample room for comparative purposes regarding the determinants of defense expenditure on both sides. The results derived in terms of input significance estimation support the findings of the earlier research as indicated above, pointing to the leading role of the demographic preponderance of Turkey over Greece. The paper also points to the fact that 10 years later, Turkey continues to set the arms race rules against its rival by determining the defense expenditure of Greece, whereas the role of the latter in affecting the military spending of Turkey is non-existent. [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]

Machine learning approaches are increasingly being employed to forecast the key characteristics of strong ground motions, including the challenging classification of Pulse-Like (PL) ground motions. The PL ground motions are characterized by their impulsive nature and have the potential to cause significant damage to structures. The classification of PL ground motions continues to be a significant challenge due to the absence of consensus on their definition and categorization. This paper investigates the potential benefits of several Machine Learning Classifiers (MLCs) algorithms such as decision tree, random forest, logistic regression, naive Bayes, support vector machine, K-nearest neighbor, ensemble model, and artificial neural network models for predicting PL and Non-Pulse-Like (NPL) ground motions. In this regard, a dataset comprising 200 near-fault ground motions records compiled from active tectonic regions like Taiwan, Turkey, Iran, and Japan, was divided into 2 portions, with 75% used to train the model and the remaining 25% used for testing. Plots of performance metrics, confusion matrix, and receiver operating curve indicate that the ensemble classifier outperforms the other classifier with 86.2% accuracy and the lowest misclassification of PL and NPL ground motions. Additionally, the trained MLC has been compared with the existing ground motion classification models to further assess the accuracy of the different classifiers in the present study. [ABSTRACT FROM AUTHOR]

The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537. [ABSTRACT FROM AUTHOR]

This paper covers the estimation of the water levels of Beysehir Lake, located in middle of Turkey, using the artificial intelligence (AI) such as the neural networks (NN) and the fuzzy logic (FL). The study considers the detailed investigation of the effect of the longterm estimate duration on the lake water level estimation. The analysed estimate ranges were 1 day, 30 days, 60 days and 90 days. The lake parameters such as the shortwave radiation, the lake total inflow rate, the lake total outflow rate and the past lake water levels constituted the input layer of the AI configurations. This study clearly showed that the estimate performance of the AI methods decreases with the increasing estimate range. It is also seen that the best estimate performance criteria are obtained by different AI methods for different estimate ranges. It is seen that the Generalized Regression Neural Network (GRNN) showed relatively superior performance compared with the other two artificial neural networks, i.e. the Radial Basis Function (RBF) and the Feed Forward Back Propagation method (FFBP), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) method, for the long estimation ranges such as 60 and 90 days. The second overall best performance was obtained by FFBP. [ABSTRACT FROM AUTHOR]

This paper studies the performance of an artificial neural network (ANN) with teaching-learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey's EED. The results of the proposed model give excellent clues with regards to its use in future energy studies. [ABSTRACT FROM AUTHOR]

Copyright of Bilgi Sosyal Bilimler Dergisi is the property of Degisim Publication 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.)

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run. [ABSTRACT FROM PUBLISHER]

Predictions of wind energy potential in a given region are based on on-location observations. The time series of these observations would later be analysed and modelled either by a probability density function (pdf) such as a Weibull curve to represent the data or recently by soft computing techniques, such as neural networks (NNs). In this paper, discrete Hilbert transform has been applied to characterize the wind sample data measured on İzmir Institute of Technology campus area which is located in Urla, İzmir, Turkey, in March 2001 and 2002. By applying discrete Hilbert transform filter, the instantaneous amplitude, phase and frequency are found, and characterization of wind speed is accomplished. Authors have also tried to estimate the hourly wind data using daily sequence by Hilbert transform technique. Results are varying. [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]

Copyright of Journal of the Institute of Social Sciences Cankiri Karatekin University / Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Cankiri Karatekin University, Institute of Social 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.)

This paper presents findings on performance benchmarking of cellular network operators in Turkey. Benchmarking is based on measurements of standard key performance indicators (KPIs) in one of the metropolitan cities of Turkey, Ankara. Performance benchmarking is formulated by incorporating customer perception by conducting surveys on how important KPIs are from the user's point of view. KPIs are measured, with standard test equipment, by drive test method on specified routes. According to the performance benchmarking results, the GSM and UMTS network operators achieving the best performance were determined in Ankara. Speech qualities of network operators, as the most popular service, were also evaluated by several statistical methods including pdf/cdf analysis and chi-square and Fisher's exact tests. The network operator providing the highest speech quality in Ankara was determined with the methods applied. Overall, the results and approaches on benchmarking of cellular networks in Turkey are reported for the first time in this paper. The approaches proposed in this paper could be adapted to wide-scale benchmarking of services in cellular networks. [ABSTRACT FROM AUTHOR]

Tourism revenues have important implications for tourism countries in terms of management of tourism-related policies. In order to accurately direct production planning, pricing, promotion and strategic marketing programs, labor and capital resources, accurate and reliable forecasts are needed. Forecasting the developments in tourism with scientific basis methods is an important guide for central and local public administration programs and tourism operators. When reviewing the literature, comparative studies on modeling and forecasting tourism revenues using Artificial Neural Networks (ANNs) are limited and this paper aims to fill this gap. Based on the gap seen in the literature, the purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when comparing the performances of three different methods namely Exponential Smoothing, Box-Jenkins and ANNs for forecasting Turkey's tourism revenues. Forecasting performances of the models were measured by MAPE statistics. As a result of the analyses performed, it was found that ANN Model with [4:5:1] architecture was the best one among the all models applied in this study. [ABSTRACT FROM AUTHOR]

This study has two purposes. Firstly, it aims to identify models that best estimate tourism demand. Secondly, it also aims to estimate the demand of Spanish tourists who visit Cappadocia, by using the identified proper models. As international tourism demand continually grows, the importance and magnitude of the tourism sector for the economies of the countries increase. In order to take into account the tourism demand, countries need to be prepared and therefore they want to know about the future demand. However, it is not always possible to know the actual demand and one can only make forecasts in such cases. This paper deals with forecasting international tourism demand, specifically focusing on the Spanish tourist visits in Cappadocia region of Turkey. In accordance with this aim, eight forecasting models are used. The results of the analyses for each model are obtained and the forecasting accuracy examined. It is seen that the Artificial Neural Networks and the Multiple Regression Model outperforms the other models. Finally, limitations of the study and future resarch directions are discussed. [ABSTRACT FROM AUTHOR]

Technological advancements coupled with growing world population require the increasing need of energy. Natural gas is one of the most important usable energy resources. Turkey is with high external dependency on energy as it has its own limited natural and underground energy resources. Thus, in order to effectively and productively use of natural gas purchased from foreign countries and to make reliable and robust energy policies for the years ahead, it is crucial to make a reasonable and plausible prediction for natural gas consumption of Turkey. In this paper, we estimate the natural gas consumption using machine learning techniques on the basis of real monthly data representing natural gas consumption of Turkey between the years 2010 and 2018. The performances of machine learning techniques involving Artificial Neural Networks, Random Forest Tree, Regression, Time Series and Multiple Seasonality Time Series are compared in predicting the natural gas consumption of Turkey. Experimental results show that among the five techniques, artificial neural networks produce the best estimation, having the lowest mean square errors, followed by regression method. Time series shows the worst performance among all the techniques. [ABSTRACT FROM AUTHOR]

This study presents a prediction methodology to assist in designing an effective resource planning for wildfire fighting. The presented methodology uses artificial neural networks, bagging and boosting in ensemble learning algorithms, and traditional machine learning algorithms decision tree regression, Gaussian process regression and support vector regression to determine the size of the area to be burned in a forest fire that will start. The Bayesian optimization algorithm, which is used in the learning process of the methods, provides the optimum hyperparameter values of the methods to obtain the minimum error value. The methodology, which is first used to predict the size of the fires that occurred in different regions of Turkey between 2015 and 2019, yielded successful results. Second, it is applied to the Montesinho Natural Park forest fire dataset in Portugal to validate its robustness in different geographical regions. Finally, the results are compared with different studies in the literature. Compared with the literature, it is seen that the presented methodology has high accuracy and high speed in the prediction of the burned area. The results of the study are significant as the proposed methodology provides valuable information to the authorized units regarding resource planning during the forest fire response phase. Furthermore, the findings show that the presented methodology is reliable and can be used as an additional tool to predict the burned area for different countries. [ABSTRACT FROM AUTHOR]

Abstract: Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy. [Copyright &y& Elsevier]

Energy analysis plays a vital role in developing an optimum and cost-effective design for the heating, ventilating and air conditioning system of a building. The values of heating degree days (HDD) and cooling degree days (CDD) are used to analyze the demand for energy for buildings. In this study, the HDD and CDD values for Turkey were calculated using estimated temperature values. An artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) were applied for the estimation of temperature values. The Levenberg–Marquardt training algorithm was used for the feedforward backpropagation of the ANN, whereas the Sugeno-type fuzzy inference system was used for the ANFIS. Meteorological data from the last 11 years were used for the proposed approaches, and these were applied to different stations (n = 50) of the Turkish State Meteorological Service. In relation to the insulated building design, base temperatures have been considered as 16 °C and 18 °C for the HDD and 22 °C and 24 °C for the CDD based on a literature review. Two novel maps were prepared for the CDD and HDD. The calculated degree days and the maps are consistent with those from the Turkish State Meteorological Service. [ABSTRACT FROM AUTHOR]

• Critical forest management decisions are strongly supported by diameter predictions. • Modern modeling techniques were used for diameter predictions along the tree bole. • Levenberg-Marquardt algorithm was investigated for ANN models construction. • Diameter at 60% of the total height was identified as the best position for calibration. • Using the Levenberg-Marquardt ANN models results in accurate diameter predictions. Critical forest management decisions are strongly supported by the knowledge of forest ecosystems and their features. There is a growing trend towards using models to predict the most accurate yield projections for forest management. In this research area, features of standing trees, such as diameters along the tree bole, are of much interest. This paper is focused on the construction and evaluation of potential modeling approaches for Scots pine stem diameter prediction in north-eastern Turkey, including (1) fixed-effects, (2) mixed-effects, (3) three and five quantile regression, and (4) an artificial neural network modeling techniques. Nonlinear fixed-effects, mixed-effects, and both quantile regression models were based on a variable exponent taper equation, while the Levenberg-Marquardt algorithm was investigated for the artificial neural network (LMANN) models construction. Parameters of the tested models were calibrated by the use of several stem diameter measurements obtained at relative height positions, with models' superior performances in diameter predictions when the information of the additional diameter measured at 60% of total height, was included. Evaluation statistics show that both quantile regression and mixed-effects models improved results as compared to fixed-effects model. Overall, LMANN models achieved the best performance with higher accuracy for the Scots pine stem diameter prediction for the whole tree as well as for sections within the tree based on the ten relative height classes. Similarly to previous findings, the results of this study support that the use of mixed-effects modeling increases flexibility and efficiency of taper equations for stem diameter prediction. Moreover, the LMANN modeling approach was evaluated as superior according to its adequacy in predicting diameters along the tree stem. Therefore, the usage of the LMANNs for the tree stem diameter prediction can be a very useful tool in forest management practice and thus worth consideration. [ABSTRACT FROM AUTHOR]

Copyright of Selcuk University Journal of Engineering, Science & Technology / Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi is the property of Selcuk University, Engineering & Architecture Faculty 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.)

Highlights • "Seddulbahir," the first Turkish national cyber-firewall system is proposed. • Artificial neural network radial basis function (RBF-NN) approach is used to generate security rules for 21 different attacks from 66 different network service flows. • National Cyber-technologies of developed countries are investigated. • Current state of the Turkey Cyber-infrastructure is highlighted. • Economic Impact of Volumetric DNS attacks on Turkey between 14th-24th December 2015 are exposed. • Required structure of the cyber-technologies to enhance country-wide cyber-security is elaborated. Abstract This research introduces "Seddulbahir," the first Turkish national cyber-firewall system. Proposed as the first of its kind, Seddulbahir resists possible cyber-attack threats against Turkey's internet infrastructure. Seddulbahir uses the artificial neural network radial basis function (RBF-NN) approach to generate rules that can detect 21 different attacks classified as probing, DoS (Denial of Service), U2R (User-to-Root), and R2L (Remote-to-Local) through the analysis of 66 different network service flows and based on three communication protocols. Results indicate that our proposed approach detects abnormal traffic with high efficiency, low cost, and has a wide detection range, providing accurate, flexible, and effective results in contrast to traditional methods. This paper also outlines the current state of cyberspace in Turkey, provides an analysis and detailed model of the latest cyber-attacks generated against Turkey, and investigates the working mechanisms of national cyber firewall systems used by developed countries such as Russia, China, and the United States. [ABSTRACT FROM AUTHOR]

The emergence of the effective management of knowledge resources as a key factor in gaining and sustaining competitive advantage presents new challenges to construction firms. Evaluating knowledge management practices is considered one of the most important challenges facing firms in today's business environment. This paper proposes a model for evaluating the knowledge management practices of construction firms. The proposed model incorporates knowledge management concepts and multilayer perceptron (MLP) neural networks to construct an importance-comparative performance analysis (ICPA) map, a simple visual tool that can provide powerful diagnostic information to executives of construction firms. The model evaluates a firm's knowledge management practices, identifies its competitive advantages and disadvantages in each knowledge management practice, and sets priorities for managerial actions to improve knowledge management practices. A real-world case study was conducted by administering a survey to 105 construction firms operating in Turkey and is presented to illustrate the implementation and utility of the proposed model. The case study findings provided preliminary support for the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies. To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope, aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC (relative operating characteristics) and cosine amplitude method ( r) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy and, or, algebraic sum maps were not sufficient to provide reliable outputs. [ABSTRACT FROM AUTHOR]

Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University 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.)

This paper investigates the modeling of the daily total global solar radiation in Adana city of Turkey using multi-linear regression (MLR), multi-nonlinear regression (MNLR) and feed-forward artificial neural network (ANN) methods. Several daily meteorological data, i.e., measured sunshine duration, air temperature and wind speed and date of the year, i.e., monthly and daily, were used as independent variables to the MLR, MNLR and ANN models. In order to determine the relationship between the total global solar radiation and other meteorological data, and also to obtain the best independent variables, the MLR and MNLR analyses were performed with the 'Stepwise' method in the Statistical Packages for the Social Sciences (SPSS) program. Thus, various models consisting of the combination of the independent variables were constructed and the best input structure was investigated. The performances of all models in the training and testing data sets were compared with the measured daily global solar radiation values. The obtained results indicated that the ANN method was better than the other methods in modeling daily total global solar radiation. For the ANN model, mean absolute error (MAE), mean absolute percentage error (MAPE), correlation coefficient ( R) and coefficient of determination ( R) for the training/testing data set were found to be 0.89/1.00 MJ/m day, 7.88/9.23%, 0.9824/0.9751, and 0.9651/0.9508, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Uniaxial compressive strength of intact rock is significantly important for engineering geology and geotechnics, because it is an important design parameter for tunnels, rock slopes rock foundations, and it is also used as input parameter in some rock mass classification systems. This paper documents the results of laboratory experiments and numerical simulations (i.e. neural network) conducted to estimate the uniaxial compressive strength of some clay-bearing rocks selected from Turkey. Emphasis was placed on assessing the role of slake durability indices and clay contents. The input variables in developed neural network (NN) model are the origin of rocks, two/four-cycle slake durability indices and clay contents, and the output is uniaxial compressive strength. It is shown that the performance of capacities of proposed NN model is quite satisfactory. However, the NN model including four cycle slake durability index yielded slightly more precise results than that including two cycle slake durability index as input parameter. The paper also presents a comparative study on the accuracy of NN model and genetic programming (GP) in the results. [ABSTRACT FROM AUTHOR]

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports. [ABSTRACT FROM AUTHOR]

In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, “ultrasound pulse velocity”, “water absorption”, “dry density”, “saturated density”, and “bulk density” which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict “uniaxial compressive strength” and “tensile strength” of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications. [ABSTRACT FROM AUTHOR]

Sustaining the human ecological benefits of surface water requires carefully planned strategies for reducing the cumulative risks posed by diverse human activities. The municipality of Aksaray city plays a key role in developing solutions to surface water management and protection in the central Anatolian part of Turkey. The responsibility to provide drinking water and sewage works, regulate the use of private land and protect public health provides the mandate and authority to take action. The present approach discusses the main sources of contamination and the result of direct wastewater discharges into the Melendiz and Karasu rivers, which recharge the Mamasın dam sites by the use of artificial neural network (ANN) modeling techniques. The present study illustrates the ability to predict and/or approve the output values of previously measured water quality parameters of the recharge and discharge areas at the Mamasin dam site by means of ANN techniques. Using the ANN model is appreciated in such environmental research. Here, the ANN is used for estimating if the field parameters are agreeable to the results of this model or not. The present study simulates a situation in the past by means of ANN. But in case any field measurements of some relative parameters at the outlet point “discharge area” have been missed, it could be possible to predict the approximate output values from the detailed periodical water quality parameters. Because of the high variance and the inherent non-linear relationship of the water quality parameters in time series, it is difficult to produce a reliable model with conventional modeling approaches. In this paper, the ANN modeling technique is used to establish a model for evaluating the change in electrical conductivity (EC) and dissolved oxygen (DO) values in recharge (input) and discharge (output) areas of the dam water under pollution risks. A general ANN modeling scheme is also recommended for the water parameters. The modeling process includes four main stages: (1) source data analysis, (2) system priming, (3) system fine-tuning and (4) model evaluation. Results of the ANN modeling scheme indicate that the output values are agreeable to the water quality parameters, which were measured at the field in the static water mass of the Mamasın dam lake. Water contamination at the dam site is caused by the continuous increase of nutrient contents and decrease of the O2 level in water causing an anaerobic condition. It may stimulate algae growth flow in such water bodies, consequently reducing water quality. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing 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.)

This paper presents an Artificial Neural Network (ANN) model for determining the total radioactivity in Hazar Lake (Sivrice, Turkey). In order to cope with complex calculations and experiments required for the determination of total radioctivity. The proposed ANN system employs the individual training strategy with fixed-weight and supervised models. The simulation demonstrate the feasibility of the neural based model. Compared to the classical methods, the proposed ANN-based model makes the processes much easier. [ABSTRACT FROM AUTHOR]

Purpose: Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies. Design/methodology/approach: Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes. Findings: The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes. Practical implications: The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents. Social implications: Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being. Originality/value: The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results. [ABSTRACT FROM AUTHOR]

Featured Application: Earthquake Detection, Earthquake Early Warning System (EEWS), Processing of Seismic data. Earthquakes threaten people, homes, and infrastructure. Earthquake detection is a complex task because it does not show any specific pattern, unlike object detection from images. Convolutional neural networks have been widely used for earthquake detection but have problems like vanishing gradients, exploding, and parameter optimization. The ensemble learning approach combines multiple models, each of which attempts to compensate for the shortcomings of the others to enhance performance. This article proposes an ensemble learning model based on a stacked normalized recurrent neural network (SNRNN) for earthquake detection. The proposed model uses three recurrent neural network models (RNN, GRU, and LSTM) with batch normalization and layer normalization. After preprocessing the waveform data, the RNN, GRU, and LSTM extract the feature map sequentially. Batch normalization and layer normalization methods take place in mini-batches and input layers for stable and faster training of the model and improving its performance. We trained and tested the proposed model on 6574 events from 2000 to 2018 (18 years) in Turkey, a highly targeted region. The SNRNN achieves RMSE values of 3.16 and 3.24 for magnitude and depth detection. The SNRNN model outperforms the three baseline models, as seen by their low RMSE values. [ABSTRACT FROM AUTHOR]

Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical tools that capture data. This study aims to investigate the relationships between dust emission (PM10) and the mean weight diameter, shear stress, and stubble amount of the soil, which were measured in eight different tillage practices (conventional tillage, six types of reduced tillage, and direct seeding). The results show that the mean weight diameter, shear stress, and stubble amount of the soil varied between 4.89 and 14.17 mm, 0.40–1.23 N·cm−2, and 30.5–158 g·m−2, respectively, depending on the type of tillage works. Additionally, dust emissions generated during different tillage applications ranged from 27.73 to 153.45 mg·m−3. The horizontal shaft rototiller produced the highest dust emission, approximately 150% higher than those of disc harrow and winged chisel plows. The impact of tillage practices on dust emission was statistically significant (p < 0.01). A sophisticated 3-(7-7)-1 ANNs model using a backpropagation learning algorithm was developed to predict the concentration of dust, which outperformed the traditional statistical models. The model was based on the values of mean weight diameter, shear stress, and stubble amount of the soil after tillage. The best result was obtained from the ANN model among the polynomial and ANN models. In the ANN model, the coefficient of determination, root mean square error, and mean error were found to be 0.98, 6.70, and 6.11%, respectively. This study demonstrated the effectiveness of ANNs in predicting the levels of dust concentration based on soil tillage data, and it highlighted the importance of adopting alternative tillage practices to reduce soil degradation and dust emissions. [ABSTRACT FROM AUTHOR]

The relationship between environmental pollution and economic growth has lately been the focus of discussion among policy makers and scientists worldwide. In Turkey, the agriculture sector is a major contributor to the national economy and consequently growth in agriculture leads to economic growth; which in turn affects pollution levels. In this paper, we present a novel application of neural networks based on using the simple and yet efficient back propagation learning algorithm. We estimate the carbon dioxide (CO 2 ) emission levels by approximating the non-linear relationship between agricultural factors and alterations in CO 2 emission levels. We use a public Turkish dataset spanning 1968-2010 and showing recorded major agricultural and economical indictors and the level of CO 2 . The experimental results indicate that such approximation is successfully possible and encouraging to be used in more similar non-linear applications. [ABSTRACT FROM AUTHOR]

Considering the importance of limited natural resources, accurately recording and evaluating temperature data is critical. The daily average temperature values obtained for the years 2019–2021 of eight highly correlated meteorological stations, characterized by mountainous and cold climate features in the northeast of Turkey, were analyzed by an artificial neural network (ANN), support vector regression (SVR), and regression tree (RT) methods. Output values produced by different machine learning methods compared with different statistical evaluation criteria and the Taylor diagram. ANN6, ANN12, medium gaussian SVR, and linear SVR were chosen as the most suitable methods, especially due to their success in estimating data at high (> 15 ℃) and low (< 0 ℃) temperatures. All the methodologies and network architectures used produced successful results (NSE-R2 > 0.90). Some deviations have been observed in the estimation results due to the decrease in the amount of heat emitted from the ground due to fresh snow, especially in the −1 ~ 5 ℃ range, where snowfall begins, in the mountainous areas characterized by heavy snowfall. In models with low neuron numbers (ANN1,2,3) in ANN architecture, the increase in the number of layers does not affect the results. However, the increase in the number of layers in models with high neuron counts positively affects the accuracy of the estimation. [ABSTRACT FROM AUTHOR]

Determining cropping patterns is crucial for quantifying irrigation water requirements at a catchment scale. For this reason, new and innovative technologies such as remote sensing (RS) and artificial neural networks (ANNs) are robust tools for generating the spatiotemporal variation of crops. In line with this, this study aims to classify each crop type using the ANN algorithm and calculate crop evapotranspiration (ETc). This study was conducted in the Akarsu Irrigation District (9495 ha) in the Lower Seyhan Plain in southeastern Turkey in the 2021 hydrological year. Crop types were classified using the ANN algorithm in the Environment for Visualizing Images (ENVI) program based on combined data from Sentinel-2 images with a 10-m resolution and ground truth data collected during the winter and summer seasons. The image analysis results demonstrated that bare soil and citrus made up 3666 ha and 3742 ha respectively in the winter season, while first crop corn (1586 ha) and citrus (4121 ha) were preponderant in summer. The confusion matrix of the ANN algorithm showed high agreement (wheat 89.76%, onion 91.67%; citrus 97.67% in winter and 98.98% in summer; 100% for lettuce, potato, sesame-2, palm, and watermelon) and medium agreement (fruit 58.33% in winter, 42.86% in summer) with ground truth data in growing seasons. Furthermore, the agreement was more than 80% for the first and second crops (cotton, soybean, peanut, and corn) in the summer season. Annual reference evapotranspiration and ETc were around 1308 mm and 890 mm, respectively. The ETc values for wheat, citrus, firstcrop corn, and second-crop soybean were found to be consistent with previous studies of direct evapotranspiration methods conducted in the Cukurova region. Overall, RS and ANNs can be used to classify crop types accurately in the growing season. This study builds upon and expands the application of RS and ANNs in large-scale irrigation schemes. [ABSTRACT FROM AUTHOR]

Precipitation is a particularly important part of the Earth's hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years. [ABSTRACT FROM AUTHOR]

This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Tree-based ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures. [ABSTRACT FROM AUTHOR]