National-Scale Flood Susceptibility Mapping of Nigeria Using Statistical and Machine Learning Models with Satellite-Driven Validation for Data-Sparse Environments

Flooding is the most recurrent and economically devastating natural hazard in Nigeria, yet no standard or consistent nationwide assessment method exists. Moreover, spatially explicit flood susceptibility information remains scarce—a critical gap for a country of over 220 million people with severely limited hydrometric monitoring infrastructure. This study presents one of the first nationwide, multi-model flood susceptibility mapping efforts for Nigeria, integrating four complementary approaches: Frequency Ratio (FR), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting (XGBoost). The framework incorporates a bivariate FR component and Height Above Nearest Drainage (HAND) as a conditioning factor. Six conditioning factors were initially evaluated—elevation, TWI, HAND, LULC, slope, and soil type—with elevation, TWI, HAND, and LULC retained for final model development. The flood inventory was derived from a HEC-RAS 100-year floodplain simulation driven by satellite-derived discharge records from the Dartmouth Flood Observatory (DFO), yielding 973,111 binary flood/non-flood observations used as training labels for the LR, RF, and XGBoost models and as a flood-pixel count reference for FR computation. The HEC-RAS floodplain was independently verified against documented DFO historical flood reports. A three-tier accuracy assessment was conducted. For statistical accuracy using a 20% test subset, XGBoost achieved the highest AUC (0.956) and Overall Accuracy (0.892). For spatial consistency against the HEC-RAS reference, LR, RF, and XGBoost achieved substantial agreement (Kappa = 0.662–0.700), while FR achieved moderate agreement (Kappa = 0.410). Validation against the 2022 Sentinel-1 SAR flood extent showed that all four models exceeded HEC-RAS flood detection accuracy. For operational flood risk management, XGBoost is recommended due to its strong predictive performance and ability to minimize missed flood-prone areas. The resulting maps provide actionable spatial intelligence for disaster risk management, land-use planning, and early warning systems across Nigeria and other data-sparse regions.