Integrating machine learning and physical models for rainfall-runoff prediction in the Upper Baro Akobo River Basin, Ethiopia
Year: 2025
Type: article
Abstract: This study aims to enhance runoff prediction accuracy in the data-scarce Upper Baro Akobo Basin, Ethiopia, by integrating the Hydrologic Engineering Center’s Hydrologic modelling System (HEC-HMS) with Support Vector Regression (SVR). Performance was evaluated using Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The hybrid HEC-HMS-SVR demonstrated superior performance with NSE of 0.989, RMSE of 29.78 m3/s, and R2 of 0.99. In contrast, the HEC-HMS model showed an NSE of 0.85, RMSE of 113.4 m3/s, and R2 of 0.89, while the SVR model had an NSE of 0.987, RMSE of 32.067 m3/s, and R2 of 0.99. The hybrid-SVR model enhances the understanding of key hydrological components, such as peak flow, base flow, hydrograph dynamics, and total runoff volume. These components are vital for effective flood management and play a crucial role in the thoughtful design and implementation of engineering solutions to address hydrological challenges.
Source: Figshare
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6902251615
FWCI:
Cites:
Cited by:
Related to: