[en] Landslides are a major geological hazard causing significant loss of life and infrastructure damage worldwide. Landslide susceptibility mapping is a crucial, though developing, tool for understanding the spatial distribution of landslide hazard. This study addresses the absence of a comprehensive landslide inventory, limited understanding of causative factors and the lack of regional-scale susceptibility maps for the Lesser Caucasus and Kura Basin (LC-KB). A landslide inventory was created for the Lesser Caucasus of Azerbaijan and compiled with other inventories, documenting 3,659 landslide polygons. Sixteen causative factors were analysed, and multicollinearity tests confirmed no significant correlations. Three Machine Learning (ML) models—Logistic Regression (LGR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost)—were fine-tuned to create landslide susceptibility maps. Slope is consistently the most influential factor across all models. Results suggest stronger influence of seismic factors than climatic ones. XGBoost achieves the highest accuracy (0.81) on the testing data set, followed by SVM (0.80) and LGR (0.73). The first two models show strong validation performance, with AUC values of 0.89 and 0.87, respectively, while LGR shows a lower AUC of 0.78. The results are vital for planning and disaster management, highlighting areas needing urgent mitigation.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Ullah, Israr ; Université de Liège - ULiège > Geology ; Institute of Neotectonics and Natural Hazards, RWTH Aachen University, Aachen, Germany
Reicherter, Klaus; Institute of Neotectonics and Natural Hazards, RWTH Aachen University, Aachen, Germany
Pánek, Tomáš; Department of Physical Geography and Geoecology, Faculty of Science, University of Ostrava, Ostrava, Czech Republic
Tibaldi, Alessandro; Department of Earth and Environmental Sciences, University of Milan-Bicocca, Milan, Italy
Al-Najjar, Husam; School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia
Kalantar, Bahareh ; RIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team, Tokyo, Japan
Ueda, Naonori; RIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team, Tokyo, Japan
Havenith, Hans-Balder ; Université de Liège - ULiège > Département de géologie > Géologie de l'environnement
Language :
English
Title :
Landslide susceptibility mapping based on data mining models in Lesser Caucasus and Kura foreland basin (Armenia and Azerbaijan)
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