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.

The Slime mould algorithm (SMA) is a relatively new metaheuristic technique that was just presented. While the performance of the newly proposed algorithms gives satisfactory results in optimization problems, combining a recently proposed algorithm with the components of different algorithms improves the performance of SMAs. In recent years, leader SMA (LSMA) and equilibrium optimizer SMA (ESMA) methods, in which SMA is combined with different algorithms, have been proposed. The advantages of the two proposed methods over SMA in different problems are shown. In this study, in order to eliminate the disadvantages of SMA, such as slow convergence rate and local optimum, the performances of the CEC2020 test functions were investigated together with the LSMA and ESMA methods proposed in recent years. The results obtained are statistically analyzed and given in detail in the study.