混沌序列WA-VOLTERRA耦合模型在月降水量预测中的应用

黄发明; 田玉刚

doi:10.3799/dqkx.2014.035

Volume 39 Issue 3

Mar. 2014

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Huang Faming, Tian Yugang, 2014. WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting. Earth Science, 39(3): 368-374. doi: 10.3799/dqkx.2014.035

Citation:

Huang Faming, Tian Yugang, 2014. WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting. Earth Science, 39(3): 368-374. doi: 10.3799/dqkx.2014.035

Huang Faming, Tian Yugang, 2014. WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting. Earth Science, 39(3): 368-374. doi: 10.3799/dqkx.2014.035

Citation:

Huang Faming, Tian Yugang, 2014. WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting. Earth Science, 39(3): 368-374. doi: 10.3799/dqkx.2014.035

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WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting

doi: 10.3799/dqkx.2014.035

Huang Faming,
Tian Yugang^,

Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Received Date: 2013-09-26
Publish Date: 2014-03-15

Abstract

Abstract

To address the inefficiency of exsiting prediction models of monthly precipitation time series due to large amount of noises and obvious characteristics of chaos, a coupling model is proposed in this study, which takes full advantages of wavelet analysis and VOLTERRA adaptive model. The monthly precipitation time series is firstly mapped into several time-frequency domains, and then a third-order VOLTERRA adaptive model is established for each domain based on the phase-space reconstruction. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective VOLTERRA adaptive model corresponding to different time-frequency domains. An experiment has been conducted by applying different models to estimate the monthly precipitation time series in Hangzhou and Nantong, and the comparison of the data obtained by the conventional model with the results obtained using wavelet analysis and support vector machine (WA-SVM) coupling prediction model confirms that this new WA-VOLTERRA coupling method can achieve higher accuracy. The new model offers a new approach for monthly precipitation forecasting.
- monthly precipitation time series,
- chaos theory,
- wavelet analysis,
- VOLTERRA adaptive model

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References(33)

References

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WA-VOLTERRA Coupling Model Based on Chaos Theory for Monthly Precipitation Forecasting

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