## 1 Introduction  
Today, driven by increased urbanization and improvements in living standards, the energy consumed in homes and living spaces is on the rise. Buildings are now responsible for about 40% of energy consumption and nearly 36% of greenhouse gas emissions in the European Union (EU). A significant portion of this energy is used for heating and cooling [1, 2]. Consequently, there has been a notable increase in the demand for fossil fuels, raising concerns about energy supply security, environmental pollution, and greenhouse gas emissions in recent years [3]. These concerns can be alleviated by employing renewable energy sources with low carbon emissions, such as biomass, solar, and geothermal energy [2, 4, 5]. Among these energy sources, geothermal energy, independent of weather conditions, has significant potential. Due to its positive attributes, geothermal energy has become increasingly important for heating, cooling, and other energy applications [6, 7, 2, 8, 9]. Moreover, geothermal energy is recognized as a renewable energy source crucial for economic development, reducing environmental pollution and greenhouse gases, and ensuring energy security [3, 10, 11]. Geothermal energy, stored beneath the Earth's crust and at its depths, originates from the natural internal heat of the planet. It has various applications, ranging from direct use to electricity production, depending on the reservoir temperature (RT). These applications include the heating and cooling of buildings, electricity generation in power plants, agricultural applications, and uses in balneology. While some applications require low- to medium-temperature fluids, others necessitate medium- to high-temperature fluids for heating, cooling, and electric power generation [3, 12]. As such, the value of RTs is crucial for determining the usability of geothermal water [12]. Hydrogeochemical analyses of geothermal waters provide insights into their formation mechanisms, enabling predictions about RTs [13]. Extensive research is vital prior to drilling in geothermal resource areas to determine the most suitable locations, which can help reduce the costs associated with geothermal drilling and enhance the utilization of geothermal resources. Identifying RTs of geothermal waters necessitates complex geological surveys and analyses, which are time-consuming, costly, and complex [14, 15, 16, 17, 18, 19, 20, 21, 22]. Machine learning techniques, successfully applied in various fields, offer a promising approach to address these challenges by predicting RTs. RTs have been predicted using methods like linear regression, linear support vector machine, and deep neural networks [12]. Additionally, there are studies on predicting geothermal heat flow and deep RTs using methods like gradient-assisted R-regression trees and artificial neural networks (ANNs) [23, 24]. ANNs are extensively used in regression problems, with various models such as feed-forward networks (FNNs) [25] and radial basic function (RBF) [26] networks found in the literature. The multilayer perceptron (MLP), a type of FNN, is particularly popular [27]. While classic optimization algorithms are commonly used, they may encounter issues like becoming stuck at local minima, premature convergence, and suboptimal performance. To overcome these issues, using metaheuristic optimization algorithms during the MLP training phase is suggested [28, 29, 30]. Among the proposed methods, whale optimization (WOA-MLP) [31], the ant lion optimizer (ALO-MLP) [32], and the slime mould algorithm (SMA-MLP) [33] are widely cited in the literature. However, because SMA has limitations in the exploration and exploitation phase due to its reliance on two random search agents, the adaptive opposition SMA (AOSMA) was introduced to mitigate these disadvantages [34]. This study seeks to accurately estimate the reservoir temperatures (RTs) of geothermal resources by leveraging hydrogeochemical data through a novel methodology.