Baseflow separation techniques for modular artificial neural network modelling in flow forecasting

In hydrological sciences there is an increasing tendency to explore and improve artificial neural network (ANN) and other data-driven forecasting models. Attempts to improve such models relate, to a large extent, to the recognized problems of their physical interpretation. The present paper deals with the problem of incorporating hydrological knowledge into the modelling process through the use of a modular architecture that takes into account the existence of various flow regimes. Three different partitioning schemes were employed: automatic classification based on clustering, temporal segmentation of the hydrograph based on an adapted baseflow separation technique, and an optimized baseflow separation filter. Three different model performance measures were analysed. Three case studies were considered. The modular models incorporating hydrological knowledge were shown to be more accurate than the traditional ANN-based models.