remote sensing Article Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach Samy Elmahdy 1,* , Tarig Ali 1 and Mohamed Mohamed 2,3 1 GIS and Mapping Laboratory, Civil Engineering Department, College of Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, UAE;
[email protected] 2 Civil and Environmental Engineering Department, United Arab Emirates University, P.O. Box 15551, Al-Ain, UAE;
[email protected] 3 National Water Center, United Arab Emirates University, Al Ain, P.O. Box 15551, Abu Dhabi, UAE * Correspondence:
[email protected]; Tel.: +971-6-515-4918 Received: 8 July 2020; Accepted: 14 August 2020; Published: 20 August 2020 Abstract: In an arid region, flash floods (FF), as a response to climate changes, are the most hazardous causing massive destruction and losses to farms, human lives and infrastructure. A first step towards securing lives and infrastructure is the susceptibility mapping and predicting of occurrence sites of FF. Several studies have been applied using an ensemble machine learning model (EMLM) but measuring FF magnitude using a hybrid approach that integrates machine learning (MCL) and geohydrological models have not been widely applied. This study aims to modify a hybrid approach by testing three machine learning models. These are boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) for FF susceptibility mapping at the northern part of the United Arab Emirates (NUAE). This is followed by applying a group of accuracy metrics (precision, recall and F1 score) and the receiving operating characteristics (ROC) curve.