## 2 Material and Methods  
### Data Acquisition  
In this study, we utilized a dataset comprising 161 data points, previously assembled by researchers within our team. This dataset, initially used to predict the application areas of reservoir temperatures (RTs) through classification approaches, is the foundation for our current investigation [17]. Herein, we propose a hybrid machine learning approach, the adaptive opposition-based slime mould algorithm-multilayer perceptron (AOSMA-MLP), applied to tackle this real-world problem by predicting the numerical values of RTs--a task commonly addressed with regression techniques in machine learning literature.  
To facilitate a comprehensive understanding of the present study, it is pertinent to briefly revisit some critical aspects of the dataset, detailed in our prior research [17]. Figure 1 illustrates the modeling's input and output parameters, encompassing seven inputs and one output. The selection of ions as input parameters is primarily influenced by their availability in the fluid, given that geothermal fluids, deriving their components from either a degassing magma heat source or the surrounding rock, often contain high concentrations of these ions. Notably, the Na+ and K+ ion concentrations are essential for understanding water-rock interactions at elevated temperatures. Moreover, an increased B content typically signifies deep feeding and a high-temperature deep reservoir. Cl\({}^{-}\) is a major anion that enhances the salinity of geothermal fluids. The silica concentration, affected by temperature, is another crucial parameter. In a geothermal system, SiO\({}_{2}\)'s solubility decreases with falling temperature. Additionally, silica is vital for estimating reservoir temperatures in thermal springs, and its precipitation can influence the operational process. The dissolved ion content in geothermal fluids, indicative of the temperature and reservoir geology of an area, varies with temperature. Low-temperature fluids contain fewer dissolved solids than their high-temperature counterparts, making electrical conductivity (EC) an important measure for assessing dissolved particles in geothermal fluids [12].  
This section meticulously details the compilation of a comprehensive dataset utilized in the current study, drawing from various sources focusing on the hydrogeochemical properties and reservoir temperatures (RTs) of geothermal resources across Turkey, particularly within Anatolia. Following is a concise rephrasing to clarify and streamline the information:  
The dataset used in this study amalgamates hydrogeochemical data and RTs from several geothermal resources across Western, Central, and Eastern Anatolia, Turkey. It integrates the following.  
Western Anatolia: a collection of 83 datasets from [12], detailing hydrogeochemical properties and RTs of geothermal resources. Central Anatolia (Camldiere): hydrogeochemical data and RTs sourced from 12 different locations, as reported by [35]. Eastern Anatolia: a selection of 42 datasets from [36], specifically focusing on hot water sources from various  
Figure 1: Input and output parameters used in modeling.  
cities and locations. Central Anatolia (Nevsehir, Kozakh): nine datasets from research documented in [37], excluding data from point 4 for consistency. Central Anatolia (Terme and Karakurt, Kirsehir): this study contributed seven datasets, excluding cold spring data but including hydrogeochemical and RT data [38]. Central Anatolia (Seydisehir, Kavak, Konya): eight datasets derived from literature [39], omitting cold spring data but incorporating measurements from April. Each dataset has been meticulously selected to ensure a robust analysis, excluding cold springs, to maintain focus on the geothermal energy potential. The range and specifics of the hydrogeochemical and RT data are tabulated in Table 1, providing a foundation for the comprehensive analysis undertaken in this study.