Evaluating Flood Vulnerability in the Loukous Basin, Northern Morocco: Integrating Machine Learning, Remote Sensing, and Climate Change Impacts

Abstract

This study investigates flood-prone areas in the Loukous Basin, Northern Morocco, utilizing machine learning and remote sensing techniques to analyze and map zones at risk. Renowned for its agricultural significance, the region features low-lying, flat terrain, an active river system, and oceanic influences, all of which exacerbate flooding risks. Additionally, climate change poses increasing challenges, intensifying extreme weather events and altering precipitation patterns that further threaten this vulnerable region. The research aims to enhance flood prevention strategies and mitigate economic and human losses by identifying and prioritizing highly vulnerable zones. Results consistently highlight significant flood susceptibility along the Loukous River and its adjacent plains, areas characterized by lowland topography, high drainage density, proximity to canals, and intensive agricultural activity. While spatial variations exist among the models, a strong consensus emerges regarding zones of low and very high vulnerability, emphasizing the need for tailored interventions. These findings provide critical insights for integrating agricultural development planning with flood risk management in the Basin of Loukous. They underscore the importance of adaptive strategies that consider the compounded effects of climate change, such as improved land-use practices, enhanced drainage systems, and sustainable water management. This study establishes a robust scientific foundation for implementing targeted measures to reduce flood impacts, safeguard livelihoods, and build resilience in this economically vital and environmentally sensitive region, with broader implications for similar flood-prone areas worldwide.