Browsing by Author "Gurgenc E."

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    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)
    (Elsevier Ltd, 2022) Varol Altay E.; Gurgenc E.; Altay O.; Dikici A.
    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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    AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Gurgenc E.; Altay O.; Altay E.V.
    Featured Application: The proposed models can help uncover the usage areas of geothermal waters by determining the reservoir temperatures in advance. Thus, they can be used as a decision support system to make the most appropriate selection. To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems. © 2024 by the authors.