Daily runoff combination prediction based on wavelet support vector machine feature classification——taking the Three Gorges Reservoir in Yichang as an example

China Rural Water and Hydropower ›› 2018 ›› (6) : 33-39.

HUANG Jing-guang ,WU Wei ,CHENG Lu-yao ,YU Nan ,CHEN Bo

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As an important prerequisite for reservoir operation and power generation, the prediction accuracy of river runoff has a direct impact on the comprehensive benefits of water conservancy projects. Based on the historical data of runoff, this paper proposes a wavelet analysis support vector machine (SVM) feature classification combined forecasting model for its volatility and randomness. Firstly, the wavelet decomposition is used to extract the high and low frequency energy spectrum of the original runoff sequence as the SVM sample mark, and the original sequence is classified by feature, dividing into stationary and abrupt sequences, the wavelet approximation signals and the detail signals, corresponding to different types of sequences, are predicted by auto-regressive moving average model (ARMA) and BP neural network model respectively, then the prediction results of each sequence are reconstructed. Finally, the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE) and the Theil Inequality Coefficient (TIC) are used as the evaluation indexes of the model. The results show that: under the 3 evaluation indexes, the proposed model is better than the ARMA and BP neural network models, and it has better prediction stability.

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runoff prediction / wavelet decomposition / support vector machine / auto-regressive moving average model / artificial neural networks / feature classification

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HUANG Jing-guang1,2,3 ,WU Wei 1,2 ,CHENG Lu-yao1 ,YU Nan1,3 ,CHEN Bo1,3. Daily runoff combination prediction based on wavelet support vector machine feature classification——taking the Three Gorges Reservoir in Yichang as an example. China Rural Water and Hydropower. 2018, 0(6): 33-39

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Received	Revised	Published
2017-12-28	2018-01-16	2018-06-15
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2019-03-22

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