Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

Samy Elmahdy,Mohamed Mohamed,Tarig Ali

doi:10.3390/rs12172695

Samy Elmahdy, Mohamed Mohamed + Show 1 more

Open Access

https://doi.org/10.3390/rs12172695

Copy DOI

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. The result demonstrated that the BRT has the highest performance for FF susceptibility mapping followed by the CART and NBT. After that, the produced FF map using the BRT was then modified by dividing it into seven basins, and a set of new FF conditioning parameters namely alluvial plain width, basin gradient and mean slope for each basin was calculated for measuring FF magnitude. The results showed that the mountainous and narrower basins (e.g., RAK, Masafi, Fujairah, and Rol Dadnah) have the highest probability occurrence of FF and FF magnitude, while the wider alluvial plains (e.g., Al Dhaid) have the lowest probability occurrence of FF and FF magnitude. The proposed approach is an effective approach to improve the susceptibility mapping of FF, landslides, land subsidence, and groundwater potentiality obtained using ensemble machine learning, which is used widely in the literature.

Highlights

Flash floods are a temporary overflow of rivers or valley plains as a natural response to unusually heavy rains
Visual inspection shows that there are some differences among the FFSM maps produced using machine learning models
The slight difference between the F1 score of the Boosted Regression Tree (BRT) and the classification and regression tree (CART) models is due to the gap between the two models and is not statistically different [87]

Summary

Introduction

Flash floods are a temporary overflow of rivers or valley plains as a natural response to unusually heavy rains. They can cause damage to infrastructure and human life [1,2]. The BRT consists of machine learning and statistical techniques designed to improve the accuracy and the performance of a single model by fitting a group of models before combining these models for classification and prediction [50]. The BRT model merges regression from classification and regression tree (CART) and boosting techniques to produce a combined modeling. Boosting is a technique designed to enhance the performance of regression trees similar to model averaging [51]. The BRT implements a stepwise process, where the models are fitted to a subset of the training dataset

Objectives

Results

Conclusion