Rainfall-runoff modeling using artificial neural networks in the Mono River basin (Benin, West Africa)

Abstract

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. However, the rainfall-runoff models mostly used in the Mono river basin struggle to better simulate high river flows. This paper presents a modeling approach based on Artificial Neural Networks (ANN) under different input meteorological parameters in the Mono River basin to better take into account the non-linearity of the relationship between rainfall and runoff. To this end, precipitation, potential evapotranspiration, and previously observed flow have been used for the daily flow simulation. The Levenberg-Marquardt algorithm is used to train the ANN rainfall-runoff models over the other optimization training algorithms mostly implemented in the study area. The analysis of the rainfall-runoff variability allowed us to show the strong correlation between rainfall and runoff and the impact of the Nangbéto dam on the flows at Athiémé. The results obtained after the training, validation, and testing of the ANN models are satisfactory (e.g., the coefficient of correlation varies between 0.93 and 0.99). The most efficient model has been identified and implemented in the Mono river basin at Nangbéto. The satisfactory results obtained show that ANN models can be considered good alternatives for traditional rainfall-runoff modeling approaches.