Abstract: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.

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