## 1 Introduction
This paper aims to clarify how ML methods produce specific results in predicting sinkhole occurrences, a novel endeavor in the sinkhole literature. The research also aims to analyze model results using diverse SHAP plots. In summary, key objectives include (i) developing a spatial ML framework for mapping sinkhole susceptibility; (ii) analyzing the relationship between the model and conditioning factors through SHAP outputs; and (iii) investigating spatial variation in model results for sinkhole susceptibility prediction in the study area. This study introduces an innovative approach by implementing an explainable ML framework to map sinkhole susceptibility in the Konya Closed Basin, Turkishye. The primary objectives include clarifying the contributions of topographic, geological-tectonic, hydrogeological, climatic, and environmental-anthropogenic factors to the susceptibility model, assessing their significance, and elucidating the decision-making rationale behind ML predictions--a novel contribution to sinkhole literature. To achieve these goals, the research employs ensemble ML algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to develop a Sinkhole Susceptibility Map (SSM). These algorithms are selected for their ability to prioritize critical factors and ensure accuracy in SSM development [34], while their inherent explainability via SHapley Additive exPlanations (SHAP) enhances model transparency [35; 36]. After training and evaluating the algorithms, the final SSM is generated using the top-performing model. The choice of ensemble methods is further justified by their robustness against noise, mitigation of overfitting, and improved model stability, which collectively enhance the reliability of susceptibility assessments [37]. By analyzing SHAP outputs, this study quantifies spatial variations in model results and investigates the interplay between conditioning factors and susceptibility predictions, addressing both the explainability and practical applicability of the framework. This study makes a dual contribution to the existing body of literature. Firstly, while RF has been widely utilized in natural hazard susceptibility studies, its application in SSM domain remains limited [17]. Conversely, the performance of newer decision-tree-based ensemble algorithms, namely XGBoost and LightGBM, has not been explored for SSM generation to the best of our knowledge. The primary aim is to comparatively evaluate the predictive performance of these three ensemble algorithms and discern their responsiveness in predicting sinkhole occurrences. Secondly, this research pioneers the integration of machine learning-based SSM generation with XAI, specifically the SHAP technique. SHAP enables analysts to understand the factors contributing more to sinkhole formation predictions, offering insights into how these factors influence single predictions [38]. This study employs a large geo-data frame with 34 independent variables adopted from previous studies and collected from 567 sinkhole locations. By bridging the black box gap, SHAP provides informative statistics and plots that reveal, among other things, the positive or negative contribution of each input factor, interaction effects in the model, and the impact of varying feature values on outcomes, including potential threshold effects. Identifying threshold values is crucial for environmental management, offering insights into how the impact of each factor changes beyond specific distance, density, or amount [39]. In short, through the application of decision tree-based ensemble ML models and SHAP, this study advances the understanding of sinkholes by providing comprehensive insights into the natural environment factors and their values that contribute more to sinkhole formation. Numerous saucepan-shaped and cylindrical sinkholes, numbering in the hundreds, have been documented and identified within the Konya Closed Basin.