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All Journal Teknika Jupiter International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Applied Data Sciences JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Alih Teknologi Sistem Informasi (JATSI) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmu Siber dan Teknologi Digital Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Herwanto, Riko
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Transportation Other

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Journal : Journal of Applied Data Sciences

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An Artificial Neural Network-Based Geo-Spatial Model for Real-Time Flood Risk Prediction Using Multi-Source High-Resolution Data Aziz, RZ Abdul; Nurpambudi, Ramadhan; Herwanto, Riko; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.913

Abstract

Flood prediction presents a pressing challenge in disaster management, especially in regions vulnerable to extreme weather events. In response, this study offers a novel approach to flood risk prediction by developing a deep learning-based Geo-Spatial Artificial Neural Network (ANN). The model actively integrates high-resolution satellite imagery, meteorological data, and topographic indicators, such as rainfall, elevation, and land use to capture complex spatial and environmental relationships that influence flood risk. This study conducted data preprocessing using Principal Component Analysis (PCA) and normalization to ensure consistency across datasets. It built the ANN with multiple hidden layers and trained it using the backpropagation algorithm on historical flood data. Furthermore, it designed the ANN model with multiple hidden layers and trained it using the backpropagation algorithm. The model achieved a notable 92% prediction accuracy, significantly outperforming traditional flood prediction methods, which typically yield 75–85% accuracy. Conventional metrics were Mean Squared Error (1.41) and R-squared (0.94). It confirmed the model’s superior ability to predict high-risk flood zones. The model also effectively captured non-linear patterns that conventional statistical or deterministic methods often failed to detect. The results showed that the model generalizes well and adapts effectively, making it suitable for real-time and data-driven flood forecasting. By integrating artificial intelligence with geo-spatial analytics, this study offers a scalable, accurate, and efficient tool for early warning systems and risk management. It recommends that future research should focus on incorporating additional data sources and refining model training techniques to further enhance scalability and performance.
Co-Authors Agus, Isnandar Ahludzikri, Fiqqi Alkahfiansyah, Muhammad Aziz, RZ. Abdul Bobby Bachry Dwi Handayani, Retno H Sudibyo, Novi Hendra Kurniawan Ketut Artaye, Ketut Lia Rosmalia M Said Hasibuan Ma'ruf, M. Hasan Muhammad Husaini Neni Purwati Nurpambudi, Ramadhan Pedliyansah, Yogi Ramaputra, Muhammad Galih Retno Dwi Handayani Rikarda, Willy Artanika RZ , Abdul Aziz Sabita, Hary Sri Karnila Suranti, Yuli Y. Suhendro YA, Firmansyah Yan Aditiya Pratama Yundari, Yundari Zaidir Jamal