dataset · Mendeley Data
This research provides an open spatial dataset for assessing soil erosion susceptibility and prioritising subwatersheds in the Mandakini River basin in India. By combining the Revised Universal Soil Loss Equation with explainable machine learning and deep learning models, the study evaluates ten environmental factors at 30-metre resolution across 23 subwatersheds. Four predictive algorithms were tested: CatBoost, Extra-Trees, MLP-ANN, and TabNet. The results indicate that around one-third of the basin falls within high or very high erosion susceptibility categories, primarily in the upper and north-central zones. Interpretability analysis revealed that the Bare Soil Index, Modified Normalized Difference Water Index, and Sediment Transport Index were the primary drivers of erosion risk. Furthermore, the analysis highlighted six specific subwatersheds, including Markanda Ganga and Mandakini River, as urgent priorities for soil conservation and erosion management interventions.
Soil erosion threatens land stability, water quality, and infrastructure in mountainous river basins. By pairing predictive algorithms with explainable tools, this research identifies the environmental triggers of erosion and maps the most vulnerable zones. This provides environmental authorities and land managers with clear geographic targets for soil conservation, helping to protect vulnerable mountain ecosystems efficiently.
The methodology and underlying dataset could support environmental consultancy firms, catchment management agencies, and spatial analytics providers in developing predictive risk-mapping tools. Because the research delivers a tested analytical framework with evaluated models and prioritised subwatersheds, it sits at an applied and tested stage, though adapting it into operational decision-support software would require further development and regional calibration.
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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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.17632/pynm8527hk
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