Proceedings of IAHS
Proceedings of IAHS
PIAHS
Articles | Volume 389
Articles | Volume 389
https://doi.org/10.5194/piahs-389-9-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-389-9-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 389
Post-conference publication
 | 
06 May 2026
Post-conference publication |  | 06 May 2026

Residual-based hybrid modeling combining GR4J and machine learning for streamflow prediction in data-scarce catchment: case of the Ouémé catchment at Bonou (Benin, West Africa)

Jérôme Enagnon Ahouandjinou, Aymar Yaovi Bossa, and Jean Hounkpe

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Short summary

This study aims to improve river flow prediction in a region where hydrological data are limited, which is essential for water management and flood preparedness. We combined a traditional rainfall–runoff model with data-driven learning methods to correct systematic simulation errors. Results show that the combined approach predicts river flow more accurately than the traditional model alone. These findings highlight a practical way to improve water resource planning in data-limited regions.

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