Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition
Yong Jun Peng · Jisuanji fangzhen · 2007
Based on wavelet analysis theory,a wavelet predictor-corrector model was proposed for the simulation and prediction of monthly discharge time series.In this model,the non-stationary time series of monthly discharge was 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 was predicted respectively through the ARMA model for stationary time series.Then the correction was conducted for the sum of the prediction results.Taking the monthly discharges at Yichang station and Cuntan station of Yangtze River as an example,the monthly discharges were simulated by using ARMA model,seasonal ARIMA model,BP artificial neural network model and the wavelet predictor-corrector model proposed,respectively.And the effect of decomposition scale for the wavelet predictor-corrector model was also discussed.It is shown that the wavelet predictor-corrector model has higher prediction accuracy than the other models and the decomposition scale has no obvious effect on the prediction for monthly discharge time series in the example.