Nonstationary hydrological time series forecasting using nonlinear dynamic methods

Recent evidence of nonstationary trends in water resources time series as result of natural and/or anthropogenic climate variability and change, has raised more interest in nonlinear dynamic system modeling methods. In this study, the effectiveness of dynamically driven recurrent neural networks (RNN) for complex time-varying water resources system modeling is investigated. An optimal dynamic RNN approach is proposed to directly forecast different nonstationary hydrological time series. The proposed method automatically selects the most optimally trained network in any case. The simulation performance of the dynamic RNN-based model is compared with the results obtained from optimal multivariate adaptive regression splines (MARS) models. It is shown that the dynamically driven RNN model can be a good alternative for the modeling of complex dynamics of a hydrological system, performing better than the MARS model on the three selected hydrological time series, namely the historical storage volumes of the Great Salt Lake, the Saint-Lawrence River flows, and the Nile River flows.