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Over the last decade, neural network-based flood forecast systems have been increasingly used in hydrological research. Usually, input data of the network are composed by past measurements of flows and rainfalls, without providing a description of the saturation state of the basin, which, in contrast, plays a key role in the rainfall-runoff process. This paper couples neural networks and fuzzy logic in order to enrich the description of the basin saturation state for flood forecasting purposes. The basin state is assessed analyzing the total rainfall occurred on a certain time window before the flood event. The proposed framework first classifies the basin saturation state providing a set of fuzzy memberships, and then issues the forecast exploiting a set of neural predictors, each specialized on certain basin saturation condition by means of a weighted least-square training algorithm. The outputs of the specialized neural predictors are linearly weighted, according to the basin state at forecast time: The more the training conditions of a predictor matches the current basin saturation state, the higher its weight on the final forecast. The framework has been tested on an Italian catchment and may overperform classical neural networks approaches.