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All Journal Jurnal Teknik BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of the Civil Engineering Forum J-Sil (Jurnal Teknik Sipil dan Lingkungan)
Olii, Rizky Selly Nazarina
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Journal : journal of the civil engineering forum

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Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning Olii, Muhammad Ramdhan; Nento, Sartan; Doda, Nurhayati; Olii, Rizky Selly Nazarina; Djafar, Haris; Pakaya, Ririn
Journal of the Civil Engineering Forum Vol. 11 No. 2 (May 2025)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.19581

Abstract

Soil erosion presents substantial environmental and economic challenges, especially in areas prone to land degradation. This study assesses the use of Machine Learning (ML) methods—Support Vector Machines (SVM) and Generalized Linear Models (GLM)—to model Soil Erosion Susceptibility (SES) in the Saddang Watershed, Indonesia. It incorporates environmental, hydrological, and topographical factors to improve prediction accuracy. The approach includes multi-source geospatial data collection, erosion inventory mapping, and relevant factor selection. SVM and GLM were applied to classify SES, with performance evaluated using accuracy, AUC, and precision metrics. Results show SVM classified 40.59% of the area as moderately susceptible and 38.50% as low susceptibility. GLM identified 24.55% as very low and 38.59% as low susceptibility. Both models demonstrated high accuracy (SVM: 87.4%, GLM: 87.2%) and strong AUC values (SVM: 0.916, GLM: 0.939), though GLM showed better specificity and recall. Feature importance analysis highlights that GLM favors hydrological factors like river proximity and drainage density, while SVM balances across various environmental inputs. These findings affirm the value of ML-based geospatial modeling for SES assessment, supporting interventions such as reforestation and erosion control. SVM is suitable for localized planning, whereas GLM offers strategic-level insights. This research contributes to advancing environmental modeling by embedding domain knowledge into ML frameworks, and suggests future work integrate real-time remote sensing and more sophisticated models for broader SES prediction.
Machine Learning Approaches to Soil Erosion Risk Mapping: A Comparison between Logistic Regression and Fast Large Margin Olii, Muhammad Ramdhan; Mokarram, Marzieh; Anshari, Erwin; Olii, Rizky Selly Nazarina; Pakaya, Ririn
Journal of the Civil Engineering Forum Vol. 12 No. 2 (May 2026)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.24796

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.
Co-Authors Ali, Azhar Zukur Putra M. Anshari, Erwin Djau, Rahman A Doda, Nurhayati Haris Djafar, Haris Ishak, Sahional Ma'sum, Ratna Dwi Mokarram, Marzieh Nento, Sartan Olii, Muhammad Ramdhan Palilati, Mifidyah Putri Rahman A. Djau Ririn Pakaya Saliko, Maxidin Talango, Novriyanti