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. Spatially explicit flood susceptibility information also remains scarce, particularly given limited hydrometric monitoring. This study comparatively evaluates national-scale flood susceptibility in Nigeria using frequency ratio (FR), logistic regression (LR), Random Forest (RF), and gradient boosting (XGBoost), while assessing conditioning-factor importance within a satellite-informed framework for data-sparse environments. Six conditioning factors were evaluated—elevation, TWI, HAND, LULC, slope, and soil type—with elevation, TWI, HAND, and LULC retained following multicollinearity and sensitivity assessments. The flood inventory was derived from a HEC-RAS 100-year floodplain simulation driven by Dartmouth Flood Observatory (DFO) satellite-derived discharge, yielding 1,048,575 binary flood/non-flood observations for supervised model development and FR computation. The HEC-RAS floodplain was qualitatively checked for spatial plausibility against documented DFO historical flood reports. On the held-out HEC-RAS-derived test subset, XGBoost achieved the highest AUC (0.956) and overall accuracy (0.892). As an internal spatial-reproduction diagnostic, LR, RF, and XGBoost showed substantial agreement with the HEC-RAS reference (Kappa = 0.662–0.700), whereas FR showed moderate agreement (Kappa = 0.410). External evaluation against the independent 2022 Sentinel-1 SAR flood extent showed consistently high flood-class detection across all four susceptibility models (93.81–94.50%). At the national scale, HAND ranked highest overall across the evaluated importance analyses. The results demonstrate the value of combining statistical and machine learning approaches with satellite-informed hydrodynamic data for national-scale flood susceptibility assessment. XGBoost showed the strongest overall predictive performance, while the resulting maps provide spatial information to support disaster risk management, land-use planning, and early warning in Nigeria and other data-sparse regions.