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All Journal Jurnal Teknik Pengairan: Journal of Water Resources Engineering Journal of the Civil Engineering Forum
Haris Djafar, Haris
Program Magister Teknik Pengairan Fakultas Teknik, Universitas Brawijaya
Civil Engineering, Building, Construction & Architecture Engineering

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Studi Analisa Kebutuhan Jumlah Stasiun Hujan Berdasarkan Evaluasi Perbandingan Antara Analisa Hidrograf Banjir Dan Banjir Historis Pada Das Limboto Provinsi Gorontalo Djafar, Haris; Limantara, Lily Montarcih; Asmaranto, Runi
Jurnal Teknik Pengairan: Journal of Water Resources Engineering Vol 5, No 2 (2014)
Publisher : Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2075.284 KB)

Abstract

In the hydrologic analysis activities, the Errors in basic hydrological data monitoring in a drainage area of the river will result in data are not correct and lead to the result of planning and management of water resources is not efficient and effective. The errors are usually caused by a number of rainfall stations in the watershed inadequate and dispersal patterns of uneven rainfall stations. The purpose of this study is to obtain the results of the evaluation of the amount of rainfall stations WMO standards based on existing conditions, to determine the comparison between the design flood discharge KaganRodda method and the design flood discharge conditions using the existing station network, and to obtain recommendations amount and location of rainfall stations positions. This study conducted in watershed of Limboto, with an area of watershed is 902.91 km2 .The results of this study are recommending 16 rainfall stations where the 4 stations is the existing stations, with each station rainfall density is 8.038 km.Keywords: Flood, Kagan-rodda, Rain Station, Rain Station density.
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.
Co-Authors Doda, Nurhayati Lily Montarcih Limantara Nento, Sartan Olii, Muhammad Ramdhan Olii, Rizky Selly Nazarina Ririn Pakaya Runi Asmaranto