Machine Learning Approaches to Soil Erosion Risk Mapping: A Comparison between Logistic Regression and Fast Large Margin - Muhammad Ramdhan Olii; Marzieh Mokarram; Erwin Anshari; Rizky Selly Nazarina Olii; Ririn Pakaya (Journal of the Civil Engineering Forum, 2026)
Soil erosion is a critical environmental issue that accelerates land degradation, reduces agricultural productivity, and increases sedimentation in water bodies. Despite its importance, spatial prediction of erosion risk remains a challenge due to the complex interaction of topographic and vegetation-related factors. Previous studies have often overlooked the integration of topographic and remote sensing indices into advanced predictive models, thereby limiting the accuracy of erosion risk mapping. This study aims to evaluate the spatial distribution of soil erosion risk in the Tamalate Watershed, Gorontalo Province, Indonesia, by integrating topographic and remote sensing conditioning factors into Logistic Regression (LR) and Fast Large Margin (FLM) models. Eight conditioning factors—Normalized Difference Moisture Index (NDMI), Terrain Ruggedness Index (TRI), Stream Power Index (STI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Tillage Index (NDTI), Topographic Wetness Index (TWI), Sediment Power Index (SPI), and Vegetation Condition Index (VCI)—were analyzed using multicollinearity diagnostics and weighted scoring to quantify their relative importance. The results revealed that NDMI (0.253 in LR; 0.258 in FLM) and TRI (0.193 in LR; 0.244 in FLM) were the most influential factors controlling erosion risk, followed by STI (0.186 in LR; 0.166 in FLM). Spatially, both models classified most of the watershed into moderate risk (44.26% in LR; 48.31% in FLM) and high risk (26.09% in LR; 22.35% in FLM) categories, while very high-risk areas were minimal (<0.2%), yet critically important for soil conservation. The findings confirm that integrating topographic and remote sensing indices enhances the precision of erosion risk assessment. This research contributes theoretically and practically by demonstrating the robustness of the FLM approach in soil erosion risk modeling and by providing spatial evidence to support land management and conservation strategies in tropical watershed environments.