The utilization of a GR4J model and wavelet-based artificial neural network for rainfall-runoff modelling
Özet
Data-driven models and conceptual models have been utilized in an attempt to perform rainfall- runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall-runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Q(t-1)) and streamflow data the preceding two days (Q(t-2)) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.
