dataset · Mendeley Data
This research provides a dataset and modelling framework evaluating soil erosion susceptibility and subwatershed management priorities in India's Mandakini River basin. Combining the Revised Universal Soil Loss Equation with machine learning, deep learning, and explainability techniques, the work examines how geological, topographic, hydrological, spectral, and land-cover factors influence erosion risk across twenty-three subwatersheds at 30-metre resolution. Models tested include CatBoost, Extra-Trees, MLP-ANN, and TabNet. Findings reveal that high and very high susceptibility spans roughly one-third of the basin, concentrated primarily in the upper and north-central sections. Explainability analysis highlighted the Bare Soil Index, Modified Normalised Difference Water Index, and Sediment Transport Index as the key drivers of model outputs. Furthermore, priority rankings consistently identified six specific subwatersheds as the most urgent targets for erosion management interventions.
Soil erosion threatens land stability, agriculture, and water quality in mountainous river basins. By combining remote sensing with interpretable machine learning, this approach clarifies which environmental factors drive erosion and pinpoints the most vulnerable areas. This offers environmental managers clearer evidence to direct soil conservation and watershed protection efforts where intervention is most urgently needed.
The work provides an applied and tested spatial modelling dataset and framework suitable for environmental consultants, land management authorities, and watershed planning bodies. It could enable more targeted planning tools and risk assessment services for soil conservation in mountainous areas. However, commercialisation would require packaging these modelling workflows and data into usable decision-support software for non-academic end users.
AI-generated from the published abstract. Always read the original work before citing.
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.1
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