Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning

Abstract

Soil erosion creates substantial environmental and economic challenges, especially in areas vulnerable to land degradation. This study investigates the use of machine learning (ML) techniques—namely Support Vector Machines (SVM) and Generalized Linear Models (GLM)—for geospatial modeling of soil erosion susceptibility (SES). By leveraging geospatial data and incorporating a range of factors including hydrological, topographical, and environmental variables, the research aims to improve the accuracy and reliability of SES predictions. Results show that the SVM model predominantly identifies areas as having moderate (40.59%) or low (38.50%) susceptibility, whereas the GLM model allocates a higher proportion to very low (24.55%) and low (38.59%) susceptibility. Both models exhibit high performance, with SVM and GLM achieving accuracies of 87.4% and 87.2%, respectively, though GLM slightly surpasses AUC (0.939 vs. 0.916). GLM places greater emphasis on hydrological factors such as distance to rivers and drainage density, while SVM provides a more balanced assessment across various variables. This study demonstrates that ML-based models can significantly enhance SES assessments, offering a more nuanced and accurate approach than traditional methods. The findings highlight the value of adopting innovative, data-driven techniques in environmental modeling and offer practical insights for land management and conservation practices.