## 1 Introduction
However, these methods' practical application is limited due to the high demand for detailed field data and their restricted scalability over large areas [9,16]. Statistical methods, on the other hand, investigate the relationships between sinkhole inventories and conditioning factors using data-driven approaches usually. Techniques such as Frequency Ratio (FR) [17, 18, 19], Logistic Regression (LR) [16, 20, 21], Linear Discriminant Analysis (LDA) [22], and Weight of Evidence (WoE) [6, 23] have been widely employed in generating sinkhole susceptibility maps. Nevertheless, these approaches generally assume linear relationships and are insufficient for modeling the complex interactions among factors. Natural hazard processes, especially geomorphological phenomena such as sinkhole formation, are characterized by complex and nonlinear dynamics. Consequently, traditional statistical methods often fail to adequately represent the interactions among the complicated geological, hydrogeological, and environmental factors involved [22]. Recently, machine learning (ML) models have proven to be a powerful alternative, addressing the shortcomings of traditional methods by effectively modeling nonlinear relationships, managing large datasets, and providing improved accuracy. Techniques such as Random Forest (RF) [7; 17; 24], Maximum Entropy (MaxEnt) model [25; 26], Support Vector Machines (SVMs) [27], Artificial Neural Network (ANN) [28], and Navie Bayes (NB) [16] have been applied to model nonlinear processes and generate more reliable and scalable sinkhole susceptibility maps. Facing a substantial obstacle, managers and planners grapple with issues related to interpretability, explainability, transparency, scientific consistency, and integration of knowledge into the ML approach while implementing ML algorithms in real-world situations [29]. These models are frequently perceived as opaque, signifying a dearth of understanding regarding their decision-making mechanisms, which result from their training on vast datasets and the utilization of intricate algorithms [30]. Studies suggest that ML methods are the primary choice for researchers and decision-makers when it comes to evaluating and mapping the sinkhole susceptibility. Furthermore, newer ML algorithms demonstrate superior computational efficiency without encountering the limitations observed in models generated by traditional statistical approaches. These advancements mitigate challenges related to prediction accuracy and utility, addressing concerns such as data quantity and quality and the inherent spatial-temporal patterns of the phenomenon and conditioning factors [16]. Nevertheless, underscoring the significance of comprehending and deciphering model results is crucial. The primary goal of XAI is to tackle the problem of non-transparent artificial intelligence (AI) systems by creating models that are easy to understand and interpret. XAI focuses on designing interactive visualization tools and frameworks that enable users to analyze and engage with AI systems, thereby clarifying how decisions are generated. The advantages of XAI are extensive, as it promotes confidence in AI technologies, ensures responsible and fair application, and helps detect and mitigate embedded biases. Implementing transparent methodologies, such as SHapley Additive exPlanations (SHAP), improves the reliability of ML models in critical applications by offering intuitive explanations of their results, thereby increasing accountability and user trust [31; 32; 33]. This study introduces an innovative approach by implementing an explainable ML method to map sinkhole susceptibility in the Konya Closed Basin, Turkishye. The main objective involves clarifying the contributions of topographic, geological-tectonic, hydrogeological, climatic, and environmental-anthropogenic factors to the susceptibility model. It includes assessing their respective significance, as well as grasping the fundamental rationale behind particular decisions.