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All Journal International Journal of Public Health Science (IJPHS) Indonesian Journal of Geography Jurnal Teknik Teras Jurnal JRST (Jurnal Riset Sains dan Teknologi) SIKLUS: Jurnal Teknik Sipil Al-sihah: The Public Health Science Journal Gorontalo Journal of Infrastructure and Science Engineering RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Insan Cita Jurnal Pengabdian Kepada Masyarakat Nusantara Science and Technology Proceedings Jurnal Teknik Sipil Cendekia (JTSC) Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian Journal of the Civil Engineering Forum Jurnal Ilmu Komputer Jurnal Teknik Sumber Daya Air Journal of Entrepreneurship and Community Innovations Jurnal Teknik Sipil RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi J-Sil (Jurnal Teknik Sipil dan Lingkungan) Jurnal Teknik Sipil dan Arsitektur
Olii, Muhammad Ramdhan
Department Of Civil Engineering, Universitas Gorontalo, Gorontalo, INDONESIA
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation

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Journal : journal of the civil engineering forum

 1 
Analysis of Spatial Distribution of the Drought Hazard Index (DHI) by Integration AHP-GIS-Remote Sensing in Gorontalo Regency Muhammad Ramdhan Olii; Aleks Olii; Ririn Pakaya
Journal of the Civil Engineering Forum Vol. 8 No. 1 (January 2022)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1802.066 KB) | DOI: 10.22146/jcef.3595

Abstract

Several regions across the world are presently experiencing a continuous increase in water scarcity due to the rise in water consumption resulting from population development, agricultural and industrial expansion, climate change, and pollution. Droughts are increasing in recurrence, severity, duration, and spatial extent as a result of climate change. Drought will be one of the most serious threats posed by climate change, often in conjunction with other effects such as rising temperatures and shifting ecosystems. Therefore, this study analyzes the spatial distribution of the Drought Hazard Index (DHI) by integrating AHP-GIS-Remote Sensing in Gorontalo Regency. AHP was used to determine the significance of each map as an input parameter for the DHI, while GIS-Remote Sensing was utilized to supply and analyze all input maps and the study outcome. The DHI assessment consists of four criteria, namely with Normalized Difference Vegetation Index accounting for the highest proportion at 42.9%, followed by Land Surface Temperature (33.6%), Normalized Difference Moisture Index (16.8%), and Topographic Wetness Index (6.7%), with the consistency of the underlying expert opinion measured by the consistency ratio of 0.048. The results indicated that the general hazard of drought in the Gorontalo Regency area was low (43.53%), with 17.87% of the whole area experiencing high hazard. The high class of drought was discovered to be centered in the central region of Gorontalo Regency, which was mostly used for agricultural and economic purposes, thereby enabling policymakers to have evidence to develop management policies suitable for local conditions. Therefore, despite the limits of climatology data, this study established the value of satellite-derived data needed to support policymakers in guiding operational actions to drought hazards reduction.
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 Abdul Djau, Rahman Abdullah, Julius Tri Octavian R. Aleks Olii Aleks Olii Aleks Olii Ali, Azhar Zukur Putra M. Anshari, Erwin Bumulo, Nasir Djau, Rahman A Doda, Nurhayati E.Poe, Isran Hano, Yanti Hz Hano, Yanti Hz. Haris Djafar, Haris Hidayat, Andi Sahrul Hippy, Mohamad Alfandi Ichsan, Ilyas Ichsan, Ilyas Ilyas Ichsan Islamuddin ismul Isnaen Muhidin, Moh. Juhra Winata Larote, Yonis Lasarudin, Alter Mangewa, Novia Michael Rorimpandey Mokarram, Marzieh Nento, Sartan Olii, Aleks Olii, Rizky Selly Nazarina Otto, Abdul Rajab Putra Olii, Muh. Yasin Umsini Rahman A. Djau Ririn Pakaya Saliko, Maxidin Santoso, Triyadi Sartan Nento Sartan Nento SYAHRIAL SYAHRIAL Syahril Panigoro Wahab, Azhar A Yunus, Hawanafisa Putri Zailani Olii, Abdul Kadir