Stormwater management modeling and machine learning for flash flood susceptibility prediction in Wadi Qows, Saudi Arabia
September 16th, 2023
Fahad Alamoudi, Mohamed Saber, Sameh A. Kantoush, Tayeb Boulmaiz, Karim I. Abdrabo, Hadir Abdelmoneim, Tetsuya Sumi
Received 2 November, 2022
Accepted 6 June, 2023
Published online 16 September, 2023
Fahad Alamoudi1)2), Mohamed Saber3), Sameh A. Kantoush3), Tayeb Boulmaiz4), Karim I. Abdrabo1)5), Hadir Abdelmoneim6), Tetsuya Sumi3)
1) Graduate School of Engineering, Kyoto University, Japan
2) Faculty of Environmental Sciences, King Abdulaziz University, Saudi Arabia
3) Disaster Prevention Research Institute, Kyoto University, Japan
4) Materials, Energy Systems Technology and Environment Laboratory, Ghardaia University, Algeria
5) Faculty of Urban and Regional Planning, Cairo University, Egypt
6) Faculty of Engineering, Alexandria University, Egypt
Predicting flash flood-prone areas is essential for proactive disaster management. However, such predictions are challenging to obtain accurately with physical hydrological models owing to the scarcity of flood observation stations and the lack of monitoring systems. This study aims to compare machine learning (ML) models (Random Forest, Light, and CatBoost) and the Personal Computer Storm Water Management Model (PCSWMM) hydrological model to predict flash flood susceptibility maps (FFSMs) in an arid region (Wadi Qows in Saudi Arabia). Nine independent factors that influence FFSMs in the study area were assessed. Approximately 300 flash flood sites were identified through a post-flood survey after the extreme flash floods of 2009 in Jeddah city. The dataset was randomly split into 70 percent for training and 30 percent for testing. The results show that the area under the receiver operating curve (ROC) values were above 95% for all tested models, indicating evident accuracy. The FFSMs developed by the ML methods show acceptable agreement with the flood inundation map created using the PCSWMM in terms of flood extension. Planners and officials can use the outcomes of this study to improve the mitigation measures for flood-prone regions in Saudi Arabia.
Copyright (c) 2023 The Author(s) CC-BY 4.0