Integrating Geomorphology-Based Terrain Segmentation with Machine Learning for Landslide Susceptibility Assessment in the Darjeeling-Sikkim Himalaya, India

Precise assessment of landslide potential in tectonically active mountain areas like Darjeeling Sikkim Himalaya (DSH) is a scientific challenge due to the complexity of different landslide conditioning factors that control the slope stability. Despite several studies for landslide susceptibility mapping, most of the conventional methods struggle to capture the nonlinear relationships and spatial heterogeneity that characterize landslides. Besides, the current use of pixel-based methods is insufficient to depict geomorphological units and slope-scale processes, thus limiting their effectiveness in boundary demarcation of landslide-prone areas. These limitations highlight the need for more robust machine learning frameworks that integrate geomorphology-based terrain segmentation with advanced machine learning models, which would not only facilitate modeling the multifaceted interactions among environmental components but also improve the understanding of the landslide driving forces. In this study, we have used slope unit based landslide susceptibility mapping with 4380 slope units integrated with 17 conditioning factors, and 8373 total updated inventories using six models Random Forest (RF), Generalized Additive Model (GAM), Categorical Boosting (CatBoost), Tabular Neural Network (TabNet), Bayesian Additive Regression Trees (BART), and Convolutional Neural Network (CNN). The model hyperparameters were optimized using Bayesian optimization, except for the BART model. Among the six models, RF (AUC = 0.848) and CatBoost (AUC = 0.846) were the best two performing models. Furthermore, SHAP analysis reveals that elevation, aspect, slope, distance to faults, NDVI, and proximity to roads and drainage networks are the main landslide controlling factors in DSH. The interaction analysis using SHAP indicates that the occurrence of landslides is controlled by nonlinear and threshold-dependent relations, especially among slope-rainfall, rainfall-soil moisture, and slope-distance to roads and faults, which represents a complex interaction between the hydrological triggering factor, geomorphic processes, tectonic activity, and human interventions.