Multi-objective performance comparison of an artificial neural network and a conceptual rainfall-runoff model

A multi-objective comparison between an artificial neural network and the conceptual HBV rainfall-runoff model has been performed. The popular NSGA-11 algorithm was used for calibration of both models. A combination of three objective functions was used to evaluate model performance. The results show that, for a small forecast lead time, the artificial neural network outperformed the HBV model on the objective functions for low and high flows, but the former was outperformed on a novel objective function related to the shape of the hydrograph. As the forecast horizon increases, the HBV model starts to outperform the ANN model on all objective functions. The main conclusion of this study is that, although the differences between the two model approaches make a straightforward and unequivocal comparison difficult, the multi-objective approach enables a more reliable evaluation of the two models than the single-objective approach.