A comparative assessment of data-driven flood susceptibility mapping in Nigeria

This study presents a nationwide assessment of flood susceptibility in Nigeria, developed to strengthen disaster risk reduction by identifying areas most prone to flooding and quantifying the population at risk. Drawing on open-access geospatial and remote-sensing data, the study compares four digital elevation models, four hydrological flow-routing methods, and three machine learning approaches to produce 48 susceptibility maps, later consolidated into an ensemble product. The analysis covers the entire country and is validated against the severe September–October 2022 floods, with results showing that approximately 11 million people live in flood‑prone areas. 

The authors recommend integrating high‑resolution susceptibility information into spatial planning, early warning systems, and targeted mitigation strategies, particularly in states where high exposure and high susceptibility coincide. Key lessons include the importance of selecting appropriate DEMs and hydrological algorithms, prioritising nature‑based solutions in floodplains, restricting urban expansion in high‑risk zones, and combining susceptibility mapping with socio‑economic vulnerability assessments. The authors also call for future work to incorporate dynamic flood modelling, updated land‑cover data, and climate projections to support long‑term resilience building.