The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition

Hui-cheng Zhou, Yong Peng () and Guo-hua Liang

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2008, vol. 22, issue 2, 217-227

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. Copyright Springer Science+Business Media, Inc. 2008

Keywords: monthly discharge prediction; wavelet decomposition; ARMA; correction (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:22:y:2008:i:2:p:217-227

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DOI: 10.1007/s11269-006-9152-x

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