The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition |
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Authors: | Hui-cheng Zhou Yong Peng Guo-hua Liang |
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Affiliation: | (1) School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning, 116023, People’s Republic of China |
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Abstract: | Based on wavelet analysis theory, a wavelet predictor-corrector model is developed for the simulation and prediction of monthly
discharge time series. In this model, the non-stationary time series of monthly discharge is decomposed into an approximated
time series and several stationary detail time series according to the principle of wavelet decomposition. Each one of the
decomposed time series is predicted, respectively, through the ARMA model for stationary time series. Then the correction
procedure is conducted for the sum of the prediction results. Taking the monthly discharge at Yichang station of Yangtse River
as an example, the monthly discharge is simulated by using ARMA model, seasonal ARIMA model, BP artificial neural network
model and the wavelet predictor-corrector model proposed in this article, respectively. And the effect of decomposition scale
for the wavelet predictor-corrector model is also discussed. It is shown that the wavelet predictor-corrector model has higher
prediction accuracy than the some other models and the decomposition scale has no obvious effect on the prediction for monthly
discharge time series in the example. |
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Keywords: | monthly discharge prediction wavelet decomposition ARMA correction |
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