Unveiling Urban Flood Vulnerability: A Machine Learning Approach for Mapping High Risk Zones in Tetouan City, Northern Morocco

Abstract

This study examines urban flood vulnerability in Tetouan city, Northern Morocco, using four machine learning models—Classification and Regression Tree (CART), Support Vector Machine (SVM), Logistic Regression (LR), and Factorial Discriminant Analysis (FDA)—to identify and map flood-prone areas. The primary goal is to enhance flood prevention efforts and minimize losses by determining the most vulnerable zones. The analysis highlights consistent flood risk along the Martil River and eastern plains, areas characterized by low-lying topography, dense drainage, proximity to canals, and recent urban development. Despite some spatial variation among the models, all consistently indicate low and very high vulnerability zones, with FDA identifying the highest proportion of very high risk areas (58%), followed by CART, SVM, and LR (39%, 38%, and 37%, respectively). In terms of model accuracy, SVM and LR outperform others, demonstrating their effectiveness in flood risk delineation. The findings offer valuable insights for urban planners and decision-makers in flood risk management, contributing to more informed resource allocation in Tetouan-Martil and potentially guiding similar strategies in comparable regions globally.