Machine Learning Approaches to Soil Erosion Risk Mapping: A Comparison between Logistic Regression and Fast Large Margin

Authors

  • Muhammad Ramdhan Olii Department of Civil Engineering, Engineering Faculty, Universitas Gorontalo, Gorontalo, INDONESIA https://orcid.org/0000-0003-3866-8615
  • Marzieh Mokarram Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, IRAN https://orcid.org/0000-0002-3514-1263
  • Erwin Anshari Department of Mining Engineering, Faculty of Mathematics and Natural Sciences, Halu Oleo University, Southeast Sulawesi, INDONESIA
  • Rizky Selly Nazarina Olii Department of Architecture, Engineering Faculty, Universitas Gorontalo, Gorontalo, INDONESIA
  • Ririn Pakaya Department of Public Health, Public Health Faculty, Universitas Gorontalo, Gorontalo, INDONESIA https://orcid.org/0000-0003-1358-4562

DOI:

https://doi.org/10.22146/jcef.24796

Keywords:

Soil erosion risk, Logistic regression, Fast large margin, Topographic indices, Remote sensing

Abstract

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.

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Published

2026-04-18

How to Cite

Olii, M. R., Mokarram, M., Anshari, E., Olii, R. S. N., & Pakaya, R. (2026). Machine Learning Approaches to Soil Erosion Risk Mapping: A Comparison between Logistic Regression and Fast Large Margin. Journal of the Civil Engineering Forum, 12(2), 267–280. https://doi.org/10.22146/jcef.24796

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Vol. 12 No. 2 (May 2026)

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