Landslide susceptibility mapping based on data mining models in Lesser Caucasus and Kura foreland basin (Armenia and Azerbaijan)

Geomatics Natural Hazards and Risk, vol. 16

Abstract

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.

Authors 8

1. Israr Ullah Aachen Institute of Neotectonics and Natural Hazards

   RWTH Aachen University

   Affiliation as printed

   Institute of Neotectonics and Natural Hazards, RWTH Aachen University

2. Klaus R. Reicherter Aachen Institute of Neotectonics and Natural Hazards

   RWTH Aachen University

   Affiliation as printed

   Institute of Neotectonics and Natural Hazards, RWTH Aachen University

3. Tomáš Pánek

   University of Ostrava

   Affiliation as printed

   Department of Physical Geography and Geoecology, Faculty of Science, University of Ostrava

4. Alessandro Tibaldi

   University of Milano-Bicocca

   Affiliation as printed

   Department of Earth and Environmental Sciences, University of Milan-Bicocca

5. Husam A. H. Al-Najjar

   University of Technology Sydney

   Affiliation as printed

   School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney

6. Bahareh Kalantar corresponding

   RIKEN Center for Advanced Intelligence Project

   Affiliation as printed

   RIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team

7. Naonori Ueda

   RIKEN Center for Advanced Intelligence Project

   Affiliation as printed

   RIKEN Center for Advanced Intelligence Project, Disaster Resilience Science Team

8. Hans‐Balder Havenith

   University of Liège

   Affiliation as printed

   Georisk & Environment, Department of Geology, University of Liege