A GeoAI-driven multi-criteria land-use suitability model of flood-prone Lagos, Nigeria

Unregulated urban expansion in Lagos coastal city is intensifying exposure to flood hazards, particularly, recurrent flooding that is linked to uncontrolled land-use transformation. This study utilized GeoAI-driven multi-criteria decision analysis (MCDA) framework to model flood-informed land-use suitability in Lagos. Geospatial data from environmental and anthropogenic factors including elevation, slope, drainage proximity, soil characteristics, land cover, and settlement density; were integrated within a hybrid GeoAI–MCDA and ML-based clustering for spatial weighting robustness, suitability assessment, and sustainable planning. Results reveal that elevation ranges between −3–4 m are associated with flood occurrence. The drainage pattern characterizes most settlements located < 99 to > 1000 m, while major residences have proximity to waterbodies at < 99 m. The MCDA flood-informed model identified extremely high-risk clusters concentrated along coastal zones, while moderate-risk areas align with rapidly urbanizing inland corridors. Findings on land-use suitability shows that highly suitable (0.01%) and moderately suitable (40.79%) are recommended for urban development, while highly-unsuitable area (59.20%) consonant with high-density settlement. A Delphi-GIS-based model classified land-use suitability into priority protection, conditional expansion, and depopulation zones. The study provide evidence-based guidance for local planners, policymakers, and global disaster-risk agencies seeking to strengthen flood-informed urban land-use regulations in Lagos, Nigeria.