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All Journal Jurnal Teknik Sipil Civil Engineering Journal Jurnal Teknik Sipil
Limantara, Lily M.
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Civil Engineering, Building, Construction & Architecture

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Journal : Civil Engineering Journal

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Evaluation of Flood Inundation Image Detection Performance Using Deep Learning Soebroto, Arief A.; Limantara, Lily M.; Suhartanto, Ery; Moh. Sholichin; Ramdani, Fatwa; Rachmawati, Turniningtyas A.
Civil Engineering Journal Vol. 11 No. 11 (2025): November
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-11-08

Abstract

Floods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems.
A Model for the Reduction of Flood Peak Discharge (ΔQp) Due to the Retarding Basin Yuwono, Hari; Limantara, Lily M.; Sholichin, Moh.; Siswoyo, Hari
Civil Engineering Journal Vol. 11 No. 12 (2025): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-12-012

Abstract

This research aims to develop a model for flood peak discharge reduction (ΔQp) through the placement of retarding basins within a watershed system, represented by the area ratio of the controlled watershed (RAk) and the maximum storage capacity of the retarding basin (Vk). The area ratio of the controlled watershed (RAk) is defined as the ratio between the catchment area of the retarding basin and the total watershed area (Ak/A). The methodology involves simulating various retarding basin placements (RAk) and different maximum storage capacities (Vk) for several flood return periods (QT). This study was conducted in the urban agglomeration area of Wonosari, Gunungkidul Regency, Special Region of Yogyakarta, Indonesia. The placement and utilization of retarding basins result in varying levels of flood peak discharge reduction (ΔQp) at the downstream control point (Taman Pancuran), depending on the maximum storage capacity of the retarding basin (Vk) and its placement within the watershed (RAk). The resulting empirical equations for flood peak discharge reduction (ΔQp) using the retarding basin method are as follows: ΔQp = 0.105654 − 0.014593 Vk − 0.029251 RAk + 0.011089 QT for Vk values in the range (V1–V4) = 36.4–208.8 × 10³ m³, and ΔQp = 1.374989 − 0.003702 Vk − 0.338381 RAk + 0.004773 QT for Vk values in the range (V4–V200) = 136.2–7039.1 × 10³ m³. An observed anomaly was identified, where ΔQp became positive at small values of Vk and RAk, indicating an increase in peak discharge (Qp).
Co-Authors Alfirman, Zulfikar R. Ery Suhartanto Fatwa Ramdani, Fatwa hari siswoyo Moh. Sholichin Moh. Sholichin Rachmawati, Turniningtyas A. Soebroto, Arief A. Sri Wahyuni Yuwono, Hari