Machine Learning Based Prediction and Analysis of Dominant Factors Driving Soil Erosion in Varanasi Watersheds

Abstract

Soil erosion remains one of the most pressing environmental challenges worldwide, with farreaching consequences for agricultural productivity, water resource quality, and ecosystem stability. Unchecked soil loss depletes fertile topsoil and accelerates sedimentation in water bodies, disrupts hydrological cycles, and contributesto land degradation. Therefore, the accurate prediction of soil erosion and identifying its dominant driving factors are crucial for effective watershed management and the development of targeted mitigation strategies. This research presents a comprehensive, data-driven investigation into the prediction and analysis of soil erosion within the Varanasi region of India, utilizing cutting-edge machine learning techniques. Data were systematically collected from 460+ distinct watersheds, incorporating 16 key parameters spanning environmental, climatic, topographic, and anthropogenic domains known to influence erosion dynamics. The study employs advanced machine learning algorithms specifically, Multivariate regression, AdaBoost regression, and Gradient boosting regression to model the complex interactions underlying soil erosion processes. To enhance model performance and interpretability, feature selection techniques are integrated into the analytical framework to identify the most significant variables contributing to erosion. These techniques reveal the predominant influence of factors such as Land Use and Land Cover (LULC), rainfall intensity, slope gradient, soil texture, and drainage characteristics. By isolating these drivers, the study not only enhances model accuracy but also provides critical insights that can guide regional land management policies and conservation planning. The results confirm the high predictive capability of the applied machine learning models, underscoring their potential as reliable tools for erosion forecasting. Furthermore, the study demonstrates that the integration of machine learning with geospatial analysis offers a powerful, scalable approach for environmental modeling. newline