Explainable Sinkhole Susceptibility Mapping Using Machine-Learning-Based SHAP: Quantifying and Comparing the Effects of Contributing Factors in Konya, Türkiye

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dc.authorid0000-0002-0881-0396
dc.authorid0000-0002-5951-0107
dc.authorid0000-0002-3341-1338
dc.authorid0000-0002-8629-1077
dc.authorid0000-0002-0609-8032
dc.contributor.authorBilgilioğlu, Süleyman Sefa
dc.contributor.authorGezgin, Cemil
dc.contributor.authorİban, Muzaffer Can
dc.contributor.authorBilgilioğlu, Hacer
dc.contributor.authorGündüz, Halil İbrahim
dc.contributor.authorArslan, Şükrü
dc.date.accessioned2025-07-14T05:28:11Z
dc.date.available2025-07-14T05:28:11Z
dc.date.issued2025
dc.departmentMühendislik Fakültesi
dc.description.abstractinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employed for sinkhole susceptibility mapping (SSM). However, the lack of explainability inherent in these methods remains a critical issue for decision-makers. In this study, sinkhole susceptibility in the Konya Closed Basin was mapped using an interpretable machine learning model based on SHapley Additive exPlanations (SHAP). The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were employed, and the interpretability of the model results was enhanced through SHAP analysis. Among the compared models, the RF model demonstrated the highest performance, achieving an accuracy of 95.5% and an AUC score of 98.8%, and was consequently selected for the development of the final susceptibility map. SHAP analyses revealed that factors such as proximity to fault lines, mean annual precipitation, and bicarbonate concentration difference are the most significant variables influencing sinkhole formation. Additionally, specific threshold values were quantified, and the critical effects of these contributing factors were analyzed in detail. This study underscores the importance of employing eXplainable Artificial Intelligence (XAI) techniques in natural hazard modeling, using SSM as an example, thereby providing decision-makers with a more reliable and comparable risk assessment.
dc.identifier.doi10.3390/app15063139
dc.identifier.issn20763417
dc.identifier.issue6
dc.identifier.scopus105001056169
dc.identifier.urihttps://dx.doi.org/10.3390/app15063139
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13276
dc.identifier.volume15
dc.identifier.wosWOS:001453383200001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorBilgilioğlu, Süleyman Sefa
dc.institutionauthorGezgin, Cemil
dc.institutionauthorBilgilioğlu, Hacer
dc.institutionauthorGündüz, Halil İbrahim
dc.institutionauthorid0000-0002-0881-0396
dc.institutionauthorid0000-0002-5951-0107
dc.institutionauthorid0000-0002-8629-1077
dc.institutionauthorid0000-0002-0609-8032
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectExplainable Artificial Intelligence
dc.subjectKarstic Hazards
dc.subjectKonya
dc.subjectRandom Forest
dc.subjectSHAP
dc.subjectSinkhole Susceptibility Mapping
dc.titleExplainable Sinkhole Susceptibility Mapping Using Machine-Learning-Based SHAP: Quantifying and Comparing the Effects of Contributing Factors in Konya, Türkiye
dc.typeArticle

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