Fahad Alamoudi, Mohamed Saber, Sameh A. Kantoush, Tayeb Boulmaiz, Karim I. Abdrabo, Hadir Abdelmoneim, Tetsuya Sumi
{"title":"Stormwater management modeling and machine learning for flash flood susceptibility prediction in Wadi Qows, Saudi Arabia","authors":"Fahad Alamoudi, Mohamed Saber, Sameh A. Kantoush, Tayeb Boulmaiz, Karim I. Abdrabo, Hadir Abdelmoneim, Tetsuya Sumi","doi":"10.3178/hrl.17.62","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrological Research Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3178/hrl.17.62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Hydrological Research Letters (HRL) is an international and trans-disciplinary electronic online journal published jointly by Japan Society of Hydrology and Water Resources (JSHWR), Japanese Association of Groundwater Hydrology (JAGH), Japanese Association of Hydrological Sciences (JAHS), and Japanese Society of Physical Hydrology (JSPH), aiming at rapid exchange and outgoing of information in these fields. The purpose is to disseminate original research findings and develop debates on a wide range of investigations on hydrology and water resources to researchers, students and the public. It also publishes reviews of various fields on hydrology and water resources and other information of interest to scientists to encourage communication and utilization of the published results. The editors welcome contributions from authors throughout the world. The decision on acceptance of a submitted manuscript is made by the journal editors on the basis of suitability of subject matter to the scope of the journal, originality of the contribution, potential impacts on societies and scientific merit. Manuscripts submitted to HRL may cover all aspects of hydrology and water resources, including research on physical and biological sciences, engineering, and social and political sciences from the aspects of hydrology and water resources.