Evaluation of CHIRPS Data for Hydrological Modeling in Data-Scarce West African Basins and Deep Learning Inflow Prediction

West Africa faces critical challenges in water and energy development due to limited ground-based hydrological data measurements. This study validates satellite data downloaded from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and develops a seasonal Long Short-Term Memory (LSTM) model for predicting dam inflow at Doma Dam, Nigeria. 37 years of CHIRPS dataset (1986-2023), integrated with the climate indices El Niño Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO), was validated against 20 years (1996-2015) ground-based records, achieving a strong correlation (r = 0.774, p < 0.001) with acceptable performance metrics: Mean Absolute Error (MAE) = 49.25 mm, Root Mean Square Error (RMSE) = 71.85 mm, and Nash-Sutcliffe Efficiency (NSE) = 0.537. The LSTM model demonstrated robust forecasting capability with R² = 0.8096, RMSE = 19.54 m³/s, and correlation = 0.9231 on test data, explaining over 75% of inflow variance while maintaining consistent performance across training and validation sets. Future predictions (2024-2030) successfully captured seasonal patterns, with wet season averaging 37.03 m³/s and dry season 10.29 m³/s. This validated CHIRPS-LSTM framework offers practical applications for hydrological forecasting in data-limited West African regions, supporting dam operations, hydropower development, water resource planning, and renewable energy development. The methodology is transferable across the Sudano-Sahelian region, demonstrating satellite data's potential for evidence-based decision-making where ground-based observations are scarce.