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)

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, naive 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.

Automated summary

Due to the increase in global climate change and depletion of fossil fuels, the interest in renewable energy sources is growing in developed countries. Geothermal energy, which can be used for both electricity production and heat energy, holds an important place among renewable energy sources. Machine learning methods were utilized in this study to predict the purpose of geothermal waters based on a geothermal dataset from different regions. Various machine learning methods were employed, and a hybrid metaheuristic artificial neural network model was developed, achieving promising results with a 91.84% accuracy rate.