Stormwater management modeling and machine learning for flash flood susceptibility prediction in Wadi Qows, Saudi Arabia

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 out-comes of this study to improve the mitigation measures for flood-prone regions in Saudi Arabia.

Automated summary

This study compares machine learning models and a hydrological model to predict flash flood susceptibility in an arid region in Saudi Arabia. The results show that the machine learning models have high accuracy and align well with the hydrological model's flood inundation map. This study is significant for improving mitigation measures for flood-prone regions in Saudi Arabia.