Abstract
PurposeThe purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.Design/methodology/approachFour machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.FindingsThe results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.Originality/valueThere are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.
Highlights
Natural disasters, including floods, pose a real threat to human life and can result in significant human losses and devastating economic consequences [1]
Drainage density was selected as input for all models while rainfall was selected for all models except x-gradient boost (XGB).TR.SE
Drainage density was a significant predictor for the random forest (RF) and k-nearest neighbors (KNN) models with scores of 98.5% and 90.9%, respectively, while digital elevation model (DEM) was an KNN.SE KNN.TR.SE artificial neural network (ANN).SE ANN.TR.SE
Summary
Natural disasters, including floods, pose a real threat to human life and can result in significant human losses and devastating economic consequences [1]. The potential magnitude of damage caused by floods has led some authors to consider them the most widespread and damaging of natural hazards [2]. Between 2000 and 2008, floods affected over 99 million people worldwide [3]. Due to heavy rainfall causing water to overflow into. © Modeste Meliho, Abdellatif Khattabi, Zejli Driss and Collins Ashianga Orlando. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
