Abstract : Flash floods pose significant hazards in urbanized zones and have important implications that should probably increase due to global changes. Early warning is thus a priority that could be done by using forecast models. When such events occur on karst basins, well known for their intrinsic complexity, anisotropy and heterogeneity, the lack of knowledge regarding the various hydrodynamic behaviours involved in karst systems prevents to use physical models. A generic black box method seems thus to be adequate; specifically, artificial neural network modelling seems to be a relevant method. To model hydrosystem behaviour efficiently, neural networks need to dispose of relevant data sets constituting input and output variables, and rigorous application of regularization methods. In this study, we propose to apply two kinds of models: feedforward and recurrent neural networks to flash flood forecasting. These models are designed using a specific methodology to diminish their complexity. They are applied to the Lez karst aquifer, located in southern France, and their performances are compared. Recurrent model can be used at longer lead time for operational flash flood forecasting. Nevertheless, for short horizon of prevision, performances of feedforward model are higher than those showed by recurrent one. The comparison of both models is then necessary to guide the improvement of operational flash flood forecasting.
