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In a context of climate disruption due to uncontrolled human activities, the classical models of rainfall - runoff modeling are almost unusable. In addition on the Lobo River (Southwest of Cote d’Ivoire), no simulation study has been carried out yet despite that, this river has flooded fields and villages causing huge losses in September 2007. Neural networks appear in this case as a solution for simulating flows in the context of non-linearity between rainfall and flow of this river. Climatic data (rainfall, temperature and PET) and land use will be phased in neural models to simulate monthly flows of the river Lobo. Four (4) neuronal model variants were constructed from three (3) hydro-climatic parameters (rainfall, potential evapotranspiration and flow) and the land acquired from Landsat ETM + 1990 and ETM + 2000. Two types of models have been created: the unguided model and the guided model. The simulation with the unguided model did not provide a satisfactory result. In effect, the value of Nash is only 22.90%. However, the NASH value of the guided model is much better than the previous one (85.01, 83.38 and 84.05%). These results help to highlight the importance of land use on the performance of neural networks. This study also demonstrated the ability of artificial neural networks to simulate the nongauged river flows in the context of climate disruption. Key words: Cote d’Ivoire, Lobo, multilayer perception, remote sensing, simulation, hydrology, model, neuron, flow.

Integration of hydro-climatic data and land use in neural networks for modeling river flows: Case of Lobo river in the southwest of Cote divoire