A nonlinear time series analysis using two‐stage genetic algorithms for streamflow forecasting

Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non‐steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two‐stage genetic algorithms were used to construct nonlinear time series models of 10‐day streamflows of the Wu‐Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications.