The Prediction Model of Chaotic Series Based on Support Vector Machine and its Application to Runoff

Abstract:

Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series. Applying the Chaos and non-linear time series based on the support vector machine regression principle, this paper proposes a method and its characteristic and the choosing of key parameters to forecast and set up models. According to Phase Space Reconstruction theory carry on reconstruction of Phase Space to monthly surface flow course, have discussed that probed into the non-linear prediction model of time series of Chaos of the support vector machine, application in the monthly surface flow, have introduce it through to the nuclear function of the base in the course of setting up the model of support vector machine, has simplified the course of solving the non-linear problems. The instance indicates that the model can deal with the complicated hydrology data array well, and there is the good prediction precision.

• Periodical: Advanced Materials Research (Volumes 255-260)
• Pages: 3594-3599
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.255-260.3594
• Online since: May 2011
• Authors: Guo Rong Yu, Ju Rui Yang, Zi Qiang Xia
• Keywords: Chaos, Hydrology Time Series, Phase Space Reconstruction, Support Vector Machine (SVM)

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