Articles | Volume 389
https://doi.org/10.5194/piahs-389-9-2026
https://doi.org/10.5194/piahs-389-9-2026
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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Cited articles

Afféwé, D. J., Merk, F., Bodjrènou, M., Rauch, M., Usman, M. N., Hounkpè, J., Bliefernicht, J. G., Akpo, A. B., Disse, M., and Adounkpè, J.: Impact of Precipitation Uncertainty on Flood Hazard Assessment in the Oueme River Basin, Hydrology, https://doi.org/10.3390/hydrology12060138, 2025. 
Amoussou, E., Awoye, H., Vodounon, H. S. T., Obahoundje, S., Camberlin, P., Diedhiou, A., Kouadio, K., Mahé, G., Houndénou, C., and Boko, M.: Climate and Extreme Rainfall Events in the Mono River Basin (West Africa): Investigating Future Changes with Regional Climate Models, Water 2020, Vol. 12, 12, https://doi.org/10.3390/W12030833, 2020. 
Biao, E. I., Alamou, E. A., and Afouda, A.: Improving rainfall–runoff modelling through the control of uncertainties under increasing climate variability in the Ouémé River basin (Benin, West Africa), Hydrol. Sci. J., 61, 2902–2915, https://doi.org/10.1080/02626667.2016.1164315, 2016. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Chen, T. and Guestrin, C.: XGBoost: A scalable tree boosting system, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17-August-2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. 
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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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