## 5 Conclusions  
Correlative relationships, given in Equation (4), based on the concentrations of radioactive elements, U, Th, and K, and radioactive heat production (RHP) were used to identify potential geothermal sites across the study area. This analysis revealed three distinct zones within the Simav Basin: western, central, and eastern regions. Migmatitic greisses from the eastern region, where geothermal energy is actively exploited, exhibited RHP values exceeding 2.0 uWkg\({}^{-1}\), whereas the central and western regions showed values below this threshold. Thus, field and laboratory analyses indicate that the eastern region has a higher potential for geothermal energy production. Notably, this area lies in proximity to seismically active zones, where recent earthquake magnitudes have exceeded 4. Statistical and unsupervised artificial intelligence (AI) models enabled the integration of magnetic susceptibility and seismic characteristics, and the concentrations of radioactive and other chemical elements. Among the statistical methods, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model effectively delineated a narrow, well-defined wedge-shaped zone, formed by overlapping clusters, that marks the most favorable subregion for further geothermal exploration and potential production. Similarly, the Autoencoder-Based Deep Clustering (ABDC) model emerged as the most promising AI model for narrowing the search area for geothermal resources. Notably, the subregions identified by both DBSCAN and ABDC encompass the existing geothermal production wells and thermal springs, which were not included as model features but served as independent validation of the model outcomes. Although these identified zones are more spatially constrained, they align well with geothermally favorable areas previously identified through field investigations and laboratory analyses. Our results demonstrate that, unlike traditional correlative methods, statistical and unsupervised AI models can incorporate magnetic and seismic data to more effectively \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Model**} \\ \hline
**Metric** & **DBSCAN GMM** & **ACM** & **ABDC** & **SOM** & **DEC** \\ \hline Silhouette Score & 0.717 & 0.433 & 0.445 & 0.340 & 0.177 & 0.134 \\ Davies–Bouldin Index & 0.392 & 0.732 & 0.684 & 1.333 & 1.680 & 2.447 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of clustering models based on evaluation metrics. narrow down potential geothermal exploration zones, providing time and cost savings. In both modeling approaches, the zones formed by the overlap of all clusters consistently highlighted the most favorable subregions for geothermal energy production.The results obtained from the AI models closely matched those from the statistical models, indicating that both methodologies can complement one another and should be jointly considered to enhance robustness. Future drilling of exploration wells and expanded geochemical sampling across the study area will be essential for validating the clustering performance of each approach. Conceptualization, E.M.I. and C.C. ; methodology, E.M.I., H.B., M.P.V. and D.C.; validation, E.M.I., C.C. and H.B. ; formal analysis, E.M.I., C.C., M.P.V., H.B. and D.C.; investigation, E.M.I. and C.C. ; resources, E.M.I., C.C. and A.A.; data curation, E.M.I. and C.C. ; writing-original draft preparation, E.M.I., C.C., M.P.V. and H.B. ; writing-review and editing, E.M.I., C.C., M.P.V. and H.B. ; visualization, E.M.I., C.C., M.P.V. and H.B. ; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research was funded by Pamukkale University Scientific Research Projects Unit grant number 2021 FEBE042. Not applicable. The data presented in this study are available on request from the corresponding author. This study is part of the first author's Ph.D. dissertation.