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All Journal Jurnal Teknik Sipil Jurnal Teknik Sipil dan Perencanaan IJOG : Indonesian Journal on Geoscience Jurnal Konstruksia Semesta Teknika Emerging Science Journal Civil Engineering Dimension Civil Engineering Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Media Ilmiah Teknik Sipil PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat EDUKATIF : JURNAL ILMU PENDIDIKAN TAPAK (Teknologi Aplikasi Konstruksi) : Jurnal Program Studi Teknik Sipil JURNAL TEKNIK HIDRAULIK Akselerasi Jurnal Ilmiah Teknik Sipil Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Prosiding Seminar Nasional Program Pengabdian Masyarakat Jurnal Konstruksi Jurnal Teknik Sumber Daya Air Sambulugana
Jazaul Ikhsan
Department Of Civil Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta,
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation

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Journal : Civil Engineering Journal

 1 
Intelligent Forecasting of Flooding Intensity Using Machine Learning Deng, Abraham Ayuen Ngong; Nursetiawan, .; Ikhsan, Jazaul; Riyadi, Slamet; Zaki, Ahmad
Civil Engineering Journal Vol 10, No 10 (2024): October
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-10-010

Abstract

This innovative study addresses critical flood prediction needs in Bor County, South Sudan, utilizing machine learning to develop an intelligent forecasting model. The research integrates diverse analytical techniques, including land use analysis and rainfall calculations, with a decade of weather data to understand complex hydrological dynamics. This research employs machine learning classifiers such as Support Vector Machines, Decision Trees, and Neural Networks. Findings reveal promising results, with the Linear SVM classifier achieving 87.5% prediction accuracy for raw data and 100% accuracy for high-velocity flooding events. The Naive Bayes classifier matched this performance, while Artificial Neural Networks showed a slight advantage in runoff estimation. The study's novelty lies in its holistic approach, combining machine learning with advanced visualization tools and geographic information systems. This creates a dynamic, real-time forecasting system bridging sophisticated analysis and practical flood management strategies. Focusing on model interpretability and multi-scale forecasting enhances its value to policymakers and disaster management authorities. This research significantly advances the application of AI to flood prediction and disaster management in offering future studies on humanitarian challenges. By enhancing early warning capabilities, this system substantially reduces flood-related losses and transforms disaster preparedness in vulnerable regions worldwide, potentially saving lives and mitigating economic impacts. Doi: 10.28991/CEJ-2024-010-10-010 Full Text: PDF
Development of Machine Learning for Debris Flow Event Prediction in a Volcanic Area Ikhsan, Jazaul; Deng, Abraham Ayuen Ngong; Mohd Arif Zainol, M. R. R.; Ibrahim, Muhammad Shazril Idris; Miyata, Shusuke
Civil Engineering Journal Vol. 12 No. 1 (2026): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-01-02

Abstract

The integration of machine learning (ML) into debris flow prediction in volcanic areas, exemplified by the Gendol River watershed of Mount Merapi, offers transformative potential for hazard mitigation. This study aimed to develop real-time, computationally efficient ML models capable of integrating multi-source data, rainfall intensity of 25 mm/hour linked to 300 cm Debris Flow heights, antecedent precipitation, and geomorphological variables to predict debris flows with actionable lead times. Key objectives included optimizing prediction accuracy, minimizing the false positive rate to 18.2% for "Debris Flow" events, and enhancing model interpretability for deployment in data-scarce volcanic regions. Results demonstrated that ensemble methods and deep learning architecture outperformed traditional models, with Efficient Logistic Regression and Linear SVM achieving an accuracy of 82.35%, and Cosine KNN attaining a prediction speed of 272 observations per second. Critical predictors included temporal rainfall patterns (contributing more than 50% to flow initiation) and ash deposit thickness (with a 70% influence on decision-making). However, challenges persisted: imbalanced datasets of nine training instances for "Debris Flow" events led to misclassification rates of 100% for hybrid events like "Rainfall and Debris Flow," while models like Naive Bayes exhibited instability (accuracy dropping to 50%). Research gaps highlighted data scarcity for high-magnitude events, limited geographic transferability, and the absence of standardized evaluation metrics. Technical limitations included reliance on low-resolution remote sensing data, high computational costs for ensemble models requiring 10 operational cost units, and the opacity of neural networks, which hindered stakeholder trust. Despite these constraints, ML models achieved 85% accuracy in non-event recognition and 76.47% precision in Bagged Trees, offering scalable frameworks for early warning systems. The study highlights the importance of enriched datasets, adaptive algorithms, and interdisciplinary collaboration in transforming volcanic risk management from a reactive approach, ultimately safeguarding vulnerable communities through data-driven, life-saving predictions.
Co-Authors Ade Prima Rivanto Ade Prima Rivanto Adhianty Nurjanah Adi Sunarto, Asril Agus Setyo Muntohar Agus Setyo Muntohar Ahmad Zaki Alfian Ma’arif Ali Nursamsi Dahlan Alidina Nurul Hidayah, Alidina Nurul Ani Hairani Ani Hairani, Ani Anjasmara, Krisna Bagus Apiniti Jotisankasa Apip Apip Apip, Apip Asril Adi Sunarto Budiarto Budiarto Burhan Barid Cahyono, Afandi Wahyu Deng, Abraham Ayuen Ngong Dyah Mutiarin Fata, Nurul Hakas Prayuda Hartono Hartono Hartono Hartono Hendra Hendra Hendy Dwi Cahyo Hilmi, Ikhlassul Hiro Agung Pratama Hung-Jiun Liao Ibrahim, Muhammad Shazril Idris Irfan Jufianto Iswanto Suwarno Jufianto, Irfan Kartika, Nia Krisna Bagus Anjasmara Lesmana, Surya Miyata, Shusuke Mohd Arif Zainol, M. R. R. Nia Kartika Nia Maharani Raharja Novika Komariona Dewi Nursamsi Dahlan, Ali Nursetiawan Ikhsan Nursetiawan Nursetiawan, Nursetiawan Nursetiawan, . Pengki Irawan Puji Harsanto Purwanto Purwanto Purwanto Purwanto Ramadhan, Muhammad Rifqi Ridwan Ardiansyah Risdiyanto Risdiyanto Rivanto, Ade Prima Rizani, Aisyah Azzahra Robial, Siti Muawanah Rohman, Fahrul Rudi Saputra Sabtanti Harimurti Sari, Amalia Kurnia Sasongko, Danang Setiati, Rehni Slamet Riyadi Slamet Riyadi Sri Atmadja Putra Rosyidi Sri Atmaja Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi Sriyadi, Sriyadi Sunarhadi, M. Amin Surya Budi Lesmana Surya Budi Lesmana Tahadjuddin Uzuoka, Ryosuke Victor G. Jetten Wahyu Widodo Wahyu Widodo Wahyudi Hidayat, Wahyudi Wawan Shodiq Purnomo