Supporting data for explainable machine learning and deep learning modeling of soil erosion susceptibility in the Mandakini River basin

This dataset supports a study that evaluates soil erosion susceptibility and subwatershed management priority in the Mandakini River basin, India, using an integrated RUSLE, machine learning, deep learning, and SHAP explainability framework. The underlying hypothesis is that soil erosion susceptibility varies systematically with geological, topographic, hydrological, spectral, and land-cover conditions, and that these relationships can be learned and interpreted using data-driven models. The dataset contains the processed modeling inventory derived from RUSLE-based soil-loss classes, ten erosion conditioning factors, model evaluation results, SHAP outputs, and subwatershed priority results for CatBoost, Extra-Trees, MLP-ANN, and TabNet. The modeling inventory was generated from spatial datasets processed at 30 m resolution and sampled across 23 subwatersheds in the basin. Supporting outputs include model performance metrics, predictor influence results, and subwatershed-level rankings. The data show that high and very high erosion susceptibility is concentrated mainly in the upper and north-central parts of the basin. Across the four models, approximately one-third of the basin was classified within the high to very high susceptibility classes. SHAP analysis identified the Bare Soil Index, Modified Normalized Difference Water Index, and Sediment Transport Index as the dominant predictors of model output. The subwatershed priority results consistently identified Markanda Ganga (SW13), Mandakini River (SW11), Bantoli Gad (SW14), Vasuki Ganga (SW10), Sina Gad (SW09), and Madhani River (SW12) as the highest-priority areas for erosion management. The dataset can be used to reproduce the reported analyses, compare model behavior, examine predictor influence, assess erosion susceptibility patterns, and support future studies on explainable remote-sensing-based erosion assessment and subwatershed prioritization in mountainous environments.