Hydrological forecasting with artificial neural networks: The state of theart

Artificial neural networks (ANN) are a novel approximation method for compl ex systems especially useful when the well-known statistical methods are no t efficient. The multilayer perceptrons have been mainly used for hydrologi cal forecasting over the last years. However, the connectionist theory and language are not much known to the hydrologist communauty. This paper aims to make up this gap. The ANN architectures and learning rules are presented to allow the best choice of their application. Stochastic methods and the neural network approach are compared in terms of methodology steps in, the context of hydrological forecasting. Recent applications in hydrology are d ocumented and discussed in the conclusion.