Comparative analysis of HEC-HMS and machine learning models for rainfall-runoff prediction in the upper Baro watershed, Ethiopia

• Published in: Hydrology Research Vol. 55; no. 9; pp. 873 - 889
• Main Authors: Belina, Yonata, Kebede, Asfaw, Masinde, Muthoni
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.09.2024
• Subjects: Accuracy; Artificial neural networks; Comparative analysis; Developing countries; Effectiveness; Error analysis; Floods; Hydrologic data; Hydrologic models; Hydrology; LDCs; Learning algorithms; Machine learning; Modelling; Neural networks; Optimization; Performance measurement; Performance prediction; Rainfall-runoff modeling; Rainfall-runoff relationships; Resource management; Root-mean-square errors; Runoff; Simulation; Stream discharge; Stream flow; Support vector machines; Water resources; Water resources management; Water shortages; Watersheds; Ethiopia
• ISSN: 0029-1277; 1998-9563; 2224-7955; 2224-7955
• DOI: 10.2166/nh.2024.032

Accurate streamflow simulation is crucial for effective hydrological management, especially in regions like the upper Baro watershed, Ethiopia, where data scarcity challenges conventional modeling approaches. This study evaluates the efficacy of three hydrological models: the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), artificial neural network (ANN), and support vector regression (SVR) in predicting runoff. Using data from 2000 to 2016, the analysis focused on various performance metrics such as the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). The results indicated that the ANN model significantly outperformed the others, achieving an NSE of 0.98, RMSE of 24 m3/s, and R2 of 0.99. In comparison, the HEC-HMS model yielded an NSE of 0.85, RMSE of 113.4 m3/s, and R2 of 0.89, while the SVR model displayed an NSE of 0.97, RMSE of 27 m3/s, and R2 of 0.99. These findings highlight the superior performance of ANN in regions with limited hydrological data, suggesting its potential as a reliable alternative to traditional physical models. By demonstrating the efficacy of machine learning models, this research facilitates the way for innovative approaches to water resource management, offering valuable insights for policymakers and practitioners.