Hybrid artificial neural network based on a metaheuristic optimization algorithm for the prediction of reservoir temperature using hydrogeochemical data of different geothermal areas in Anatolia (Turkey)
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2022
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Abstract
Due to the increase in the changes in global climate in recent years and the depletion of fossil fuels, the interest in renewable energy sources in many developed countries is increasing day by day. Among the renewable energy sources, geothermal energy has an important place because it can be used both in electricity production and directly as heat energy. Before using geothermal fluids, it is necessary to determine their properties by making detailed geological studies and thus to determine the most suitable drilling location. These processes are very costly, time-consuming, and require special equipment. Such disadvantages can be eliminated by using machine learning methods. In this study, the machine learning methods developed for the classification approach were used to predict the purpose of the geothermal waters with the help of the geothermal data set obtained from different regions. In this study, naïve Bayes classifier, K-nearest neighbor, linear discrimination analysis, binary decision tree, support vector machine, and artificial neural network, which are widely used in the literature, were used. In addition, promising results were obtained by designing a hybrid metaheuristic artificial neural network model. While an accuracy in traditional machine learning methods between 71% and 82% was obtained, a 91.84% accuracy was obtained in the model proposed. © 2022 Elsevier Ltd
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Anatolia , Turkey , Classification (of information) , Classifiers , Decision trees , Fossil fuels , Geothermal fields , Learning algorithms , Nearest neighbor search , Support vector machines , Classification approach , Hybrid artificial neural network , Hybrid metaheuristic artificial neural network , Hybrid metaheuristics , Hydro geochemistries , Machine learning algorithms , Machine learning methods , Network-based , Renewable energy source , Reservoir temperatures , accuracy assessment , algorithm , artificial neural network , classification , geothermal energy , geothermal system , hydrogeochemistry , optimization , prediction , Neural networks