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

Abstract

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.

Keywords

West Africa / Streamflow prediction / Machine learning / Support vector machine / Regional river flow

Acknowledgments

The authors acknowledge support from the Department of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria, Nigeria.

Author Contributions

A.-A.D.B.: Conceptualization, Methodology, software, writing—original draft; B.U.A. Writing—validation, review, and editing; A.S: Visualization and editing; B.T.: writing—Data curation and editing; K.S: writing—review and editing; A.I.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Original data and code are available from the corresponding author upon reasonable request.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare no conflicts of interest.

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