Harnessing InSAR and Machine Learning for Geotectonic Unit-Specific Landslide Susceptibility Mapping: The Case of Western Greece

Landslides are one of the most severe geohazards globally, causing extreme financial and social losses. While InSAR time-series analyses provide valuable insights into landslide detection, mapping, and monitoring, AI is also implemented in a variety of geohazards, including landslides. In the present study, a machine learning (ML) landslide susceptibility map is proposed that integrates the geotectonic units of Greece and incorporates various sources of landslide data. Satellite data from Persistent Scatterer Interferometry analysis, validated by geotechnical experts, resulted in an extremely large dataset of more than 3000 landslides in an area of interest, including the most landslide-prone area in Greece. The gradient-boosted decision tree was employed in the landslide susceptibility mapping. The model was trained on three geotectonic units and five prefectures of Western Greece and performed well in predicting landslide events. Finally, a SHAP (SHapley Additive exPlanations) analysis verified that precipitation and geology, which are the main landslide-triggering and preparatory factors, respectively, in Greece, positively affected landslide characterization. The innovation of the proposed research lies in the uniqueness of this newly created dataset, comprising a remarkably large number of landslide and non-landslide locations in Western Greece. By adopting a strict machine learning methodology, the spatial autocorrelation effect, which is overlooked in similar studies, was reduced. Also, leveraging the unique features of the geological formations, the model was trained to incorporate differences in the landslide susceptibility of formations located in different geotectonic units with variant geotechnical characteristics. The proposed approach facilitates the generalization of the model and sets a strong base for the creation of a national-scale landslide susceptibility mapping and forecasting system.