ILEVBARE Godson Ehimiyei

Flooding along the River Niger remains a persistent hydrological and humanitarian challenge, exacerbated by data scarcity, climate intensification, and rapid land-cover transformation. This study advances a sustainable framework for flood prediction and risk mapping by integrating geospatial technologies with deep learning in data-limited riparian environments. The study focused on the Agenebode–Idah corridor, delineated by a 20 km buffer on either side of the River Niger. The research aimed to design, train, and validate a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN– LSTM) model capable of representing both the spatial complexity and temporal evolution of flood dynamics. The overarching purpose was to enhance anticipatory flood governance and inform climate-resilient development strategies across vulnerable communities of the lower Niger Basin. Methodologically, the study employed a multi-source geospatial architecture integrating CHIRPS rainfall, ERA5 soil moisture and temperature, SRTM topography, Sentinel-1 SAR inundation data, and Landsat-derived land-use/land-cover information. The CNN component extracted geomorphological and hydrological features, including elevation, slope, vegetation, and proximity to rivers, while the LSTM component learned the sequential dependencies of hydroclimatic variables over time. Model calibration and validation employed rigorous statistical and spatial metrics, including ROC–AUC, F1- score, RMSE, and confusion matrix analysis, alongside geospatial overlays between predicted and observed flood masks. The framework simulated both historical flood dynamics (2000–2024) and flood probabilities (2026–2035), allowing for a comprehensive spatiotemporal risk assessment. Results revealed that the hybrid CNN–LSTM achieved high predictive fidelity (ROC– AUC = 0.92; F1 = 0.86; RMSE = 0.081), outperforming baseline machine-learning and hydrodynamic models. Spatial analyses identified chronic flood hotspots in Agenebode, Anegbete, Ifeku Island, Ogurugu, and Idah. Forward projections indicated a 34% spatial expansion of high-risk zones by 2035, primarily across cropland and peri-urban settlements. The study concludes that coupling AI-driven deep learning with geospatial intelligence enhances flood forecasting capacity in data-scarce regions, providing a replicable, operational blueprint for early warning systems, spatial planning, and adaptive water governance in the face of accelerating climate variability.