## 6 Conclusions  
This study aims to model sinkhole susceptibility in the Konya Closed Basin using an explainable artificial intelligence (XAI) approach. For this purpose, models created using RF, XGBoost, and LightGBM algorithms were compared, and the RF model, with the highest accuracy (95.5%), was preferred for generating the susceptibility map of the results. SHAP analyses have shown that geological-tectonic (proximity to faults, lithology), hydrogeological (bicarbonate difference, groundwater level change), and climatic (annual average precipitation) factors are the most essential factors in sinkhole formation. In this study, specific threshold values have been identified. Sinkhole formation significantly increases within a distance of up to 2000 m from faults. The annual average precipitation ranging between 33 and 36 mm is a critical factor contributing to the increase in sinkhole formation in the region. In addition to that, a bicarbonate difference exceeding 0.68 indicates that changes in groundwater chemistry directly contribute to sinkhole formation. These findings provide important insights for urban planning, infrastructure investments, agricultural water management, and the sustainability of human life. In particular, the SHAP-based explainability approach has facilitated easier interpretation of sinkhole susceptibility analyses by decision-makers and has made the model outputs more understandable. This study demonstrates the importance of using XAI techniques in natural disaster modeling and enables more reliable decision-making in risk management strategies. In conclusion, this research provides a robust explainable machine learning framework to better understand sinkhole formation in karst areas and minimize potential risks that threaten sustainability.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15063139/s1](https://www.mdpi.com/article/10.3390/app15063139/s1)]([https://www.mdpi.com/article/10.3390/app15063139/s1](https://www.mdpi.com/article/10.3390/app15063139/s1)), Table S1: Multi-collinearity test results involving all criteria; Table S2: Multi-collinearity test results involving selected criteria after the sequential elimination; Figure S1: The Pearson's correlation coefficients.  
Conceptualization, S.S.B., M.C.I., S.A. and C.G.; methodology, S.S.B. and M.C.I.; software, S.S.B. and M.C.I.; validation, S.S.B., C.G. and M.C.I.; formal analysis, S.S.B., C.G. and M.C.I.; investigation, S.S.B., C.G., M.C.I., H.B. and H.I.G.; data curation, S.S.B., C.G., H.B. and H.I.G.; writing--original draft preparation, S.S.B., C.G., M.C.I., H.B., H.I.G. and S.A.; writing--review and editing, S.S.B., C.G., M.C.I., H.B., H.I.G. and S.A.; visualization, S.S.B., C.G. and M.C.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Konya Provincial Directorate of Turkish Disaster and Emergency Management Presidency (AFAD) within the framework of the \"Determination of Sinkhole Locations Project\" [grant no. 2020K-138637].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The author thank the journal editor and anonymous reviewers for constructive comments. Special thanks to Konya Provincial Directorate of AFAD (Turkish Disaster and Emergency Management Presidency) for project funding.  
The authors declare no conflicts of interest.