Soil erosion susceptibility modeling using stacking ensemble machine learning, Kangra District, north-western Himalaya, India

The present study aims to predict soil erosion-prone regions in Kangra District using ML algorithms; Support Vector Machine (SVM), Random Forest (RF), XGBoost and CatBoost and their stacking ensembles. Additionally, the study also utilizes the SHAP (SHapley Additive exPlanations) model to enhance the interpretability of the best-performing RF algorithm. For this, a comprehensive soil erosion point dataset was developed using empirical field survey integrated with visual interpretation of Google Earth historical images. The results show RF as the best-performing model, predicting 14.7% of the area into the very high susceptibility zone, followed by XGBoost (27.1%), SVM (24.4%) and CatBoost (16.4%), whereas the stacking ensemble predicted 25.2%. The SHAP analysis of the best-performing model (RF) indicates NDVI, slope and LULC as the primary governing factors influencing soil erosion in the region. This study also provides a scientific base for policy formulation and strategic planning to mitigate soil erosion in Kangra District.