Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

International audienceThe application of Artificial Neural Networks (ANNs) on rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling in the Geer catchment (Belgium) using both daily and hourly data. The good daily forecast results indicate that ANNs can be considered alternatives for traditional rainfall-runoff modelling approaches. However, investigation of the forecasts based on hourly data reveal a constraint that has hitherto been neglected by hydrologists. A timing error occurs due to a dominating autoregressive component that is introduced by using previous runoff values as ANN model input. The reason for the popular practice of using these previous runoff data is that this information indirectly represents the hydrological state of the catchment. Two possible solutions to this timing problem are discussed. Firstly, several alternatives for representation of the hydrological state are presented: moving averages over the previous discharge and over the previous rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these various hydrological state representators produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the use of a combination of multiple measures of model performance during ANN training is suggested, since not all differences between modelled and observed hydrograph characteristics such as timing, volume, and absolute values can be adequately expressed by a single performance measure. The possible undervaluation of timing errors by the commonly-used squared-error-based functions is a clear example of this inability