## 5 Discussion
### Explainable AI in Sinkhole Susceptibility: Insights from SHAP  
The \"black-box\" nature of machine learning (ML) models has long been a critical issue in natural hazard susceptibility mapping, as it limits the interpretability and trustworthiness of model predictions [30; 64]. To address this challenge, explainable artificial intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have emerged as powerful tools for enhancing model transparency [32; 84]. SHAP provides both global and local explanations by quantifying the contribution of each input feature to the model's predictions, making it particularly valuable for understanding complex, nonlinear relationships in geospatial data [38; 85]. In this study, we leverage SHAP to transition from traditional \"black-box\" models to an explainable framework, marking a significant advancement in sinkhole susceptibility mapping. By applying SHAP, we not only identify the most influential factors but also reveal how specific values of these factors contribute to sinkhole formation at both regional and local scales. This dual-level explanation provides a more reliable basis for decision-making, particularly in the Konya Closed Basin, where understanding the underlying drivers of sinkhole formation is crucial for sustainable land-use planning and risk mitigation. The following analysis explores the SHAP-based interpretation of key factors influencing sinkhole susceptibility, offering actionable insights for stakeholders and policymakers. According to the results obtained from the SHAP analysis, the five key factors were identified as proximity to fault lines, annual average rainfall, bicarbonate difference, proximity to drainage lines, and well density. Sinkhole formation reaches its highest levels in areas up to approximately 2000 m from fault lines. The literature also indicates that fault lines create fractured zones, which accelerate groundwater movement and, consequently,increase the risk of karst collapse [2]. Additionally, in karst regions, fault zones have low impermeability, allowing water to rapidly infiltrate underground, which in turn enhances rock dissolution [99]. Annual average rainfall ranging between 33 and 36 mm supports the findings that consistent water flow accelerates karst dissolution and triggers the sinkhole formation process [100]. Moreover, it has been stated that both low and excessive rainfall in karst systems directly affect dissolution and mechanical erosion processes [101]. Particularly, changes in seasonal rainfall patterns and sudden rainfall following periods of extreme drought disrupt the stability of underground voids, enhancing sinkhole formation [102]. Another key factor, the bicarbonate difference being above 0.68, indicates that changes in the chemical composition of water contribute directly to sinkhole formation by dissolving carbonate rocks more rapidly. Previous studies have also noted that the high bicarbonate content in groundwater weakens ground stability by corroding carbonate rocks [103]. It can be stated that changes in groundwater chemistry, particularly, lead to the acceleration of karstic processes, which in turn increase the risk of sudden collapse in the long term. Areas up to approximately 2000 m from drainage lines fall within the group of areas most susceptible to sinkhole formation. It has been stated that drainage systems alter water flow paths, leading to sudden fluctuations in groundwater levels, and the interaction between drainage lines and groundwater movement accelerates the karstic cavities formation process, increasing sinkhole susceptibility [104]. The density of wells is one of the most important factors directly influencing sinkhole formation. Specifically, uncontrolled wells drilled for agricultural irrigation lead to significant declines in groundwater levels, destabilizing the ground and increasing sinkhole formation.