Multi-Scale, Explainable XGBoost Landslide Susceptibility Mapping: From Watershed-Scale Controls to EGMS–GNSS–Rainfall Validation of Active Instabilities in the Western Corinth Rift

Landslides in the Western Corinth Rift reflect a mix of long-term “set-up” conditions—such as terrain, rock type, and fault-related structure—and short-term triggers linked to transient deformation and changing rainfall patterns. To represent these interacting processes in a clear and interpretable way, we propose a two-phase, multi-scale landslide susceptibility workflow based on explainable XGBoost. At Phase 1 (watershed scale) we develop a baseline susceptibility model using a standardized set of conditioning factors. These include (i) terrain and geomorphometric variables (elevation, slope, aspect, profile curvature, plan curvature and topographic wetness index (TWI) and (ii) lithological and structural controls (lithology and hydrolithology classes, distance from river network and fault-influence proxies such as distance to faults). The model is trained using historical landslide inventories, whereas interpretability was built in through explainable AI tools, such as SHAP, allowing us to quantify both global and site-specific contributions of conditioning factors, including key interactions. The result is a set of susceptibility maps paired with readable diagnostics that explain why certain areas are critical. At Phase 2 (local refinement and activity confirmation) focuses on the Krini–Gkrekas–Pititsa sector, where observations are denser and more reliable. Here, we evaluate whether susceptibility hotspots from Phase 1 align with evidence of ongoing or emerging instability. We add dynamic indicators and independent validation using: European Ground Motion Service InSAR ground motion, SBAS historical InSAR data; GNSS trend metrics and antecedent precipitation indices from station data. The goal is not just to refine local interpretation, but to test whether predicted patterns make physical sense, by checking consistency between (a) areas predisposed by lithology and structure and (b) present-day deformation signals and rainfall forcing. The workflow aims to produce decision-ready, interpretable outputs at two complementary scales: (1) watershed-scale susceptibility that highlights where failures are more likely based on relatively stable controls, and (2) a localized assessment that strengthens confidence where susceptibility coincides with measured deformation and hydrometeorological conditions. This improves trust and usability of AI-assisted landslide hazard assessment in tectonically active landscapes.

Keywords

Landslide susceptibility; XGBoost; explainable AI; SHAP; multi-scale modeling; watershed analysis; lithology; active faults; EGMS; InSAR ground motion; GNSS; antecedent precipitation index; Western Corinth Rift.