DocumentCode :
1944162
Title :
Research on Hydrology Time Series Prediction Based on Grey Theory and epsilon-Support Vector Regression
Author :
Zhao Cheng-Ping ; Liang Chuan ; Guo Hai-wei
Author_Institution :
Coll. of Water Resources & Hydropower, Sichuan Univ., Chengdu, China
fYear :
2011
fDate :
5-7 Aug. 2011
Firstpage :
968
Lastpage :
971
Abstract :
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined prediction model based on support vector machine and grey theory was proposed. The grey time series prediction method was used to reduce complexity of samples and the support vector machine regression was used to reduce complexity of prediction model. The incoming water time series of Minjiang River in 1937-2002 were taken as the sample to be analyzed. The results show that the combined algorithm of e-support vector regression and grey theory has better effects in simulate of trend data and the random data in medium and long-term forecasting.
Keywords :
grey systems; hydrology; regression analysis; rivers; support vector machines; time series; Minjiang river; epsilon-support vector regression; forecasting accuracy; grey theory; hydrology time series prediction; imbalance complexity; support vector machine; Biological system modeling; Data models; Kernel; Mathematical model; Predictive models; Support vector machines; Time series analysis; accuracy; complexity; grey theory model; support vector regression; time series prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
Type :
conf
DOI :
10.1109/ICDMA.2011.240
Filename :
6052073
Link To Document :
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