Enhancing Streamflow Forecasting in Major West African Rivers by Utilizing Meta-Heuristic Algorithms and Climate Data Time Lag Analysis

Accurate streamflow prediction is essential for irrigation planning, water allocation, and flood risk management, particularly in water-scarce regions like the Niger River Basin. However, the complexity of hydrological processes and data limitations make reliable predictions challenging. This study optimizes Support Vector Machine (SVM) hyperparameters for daily streamflow prediction using time-lagged climate data and four metaheuristic algorithms—Binary Slime Mould Algorithm (BSMA), African Vulture Optimisation Algorithm (AVOA), Archery Algorithm (AA), and Intelligent Ice Fishing Algorithm (IIFA). Model performance was assessed using eight evaluation metrics, with results showing that AA and IIFA consistently outperform the others, achieving Nash-Sutcliffe Efficiency (NSE) values between 0.986–0.999 and 0.893–0.999, respectively. AVOA and BSMA show less consistent performance, with NSE ranges of 0.524–0.999 and 0.863–0.965, respectively. The study highlights the novel integration of multiple metaheuristic algorithms for optimizing machine learning models, offering insights into their effectiveness for hydrological prediction. By demonstrating the superior performance of AA and IIFA, this research provides a robust framework for enhancing long-term streamflow forecasting. These findings support improved water resource management in West Africa, helping policymakers address climate variability, water scarcity, and hydrological uncertainty.