基于整体经验模态分解和支持向量回归的北京市PM2.5预测

doi:10.13278/j.cnki.jjuese.201602206

Journal of Jilin University(Earth Science Edition) ›› 2016, Vol. 46 ›› Issue (2): 563-568.doi: 10.13278/j.cnki.jjuese.201602206

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PM2.5 Prediction of Beijing City Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

Qin Xiwen^1,2,3, Liu Yuanyuan², Wang Xinmin², Dong Xiaogang², Zhang Yu², Zhou Hongmei²

1. Graduate School, Changchun University of Technology, Changchun 130012, China;
2. School of Basic Sciences, Changchun University of Technology, Changchun 130012, China;
3. Automotive Engineering Research Institute, Changchun University of Technology, Changchun 130012, China

Received:2015-07-01 Published:2016-03-26
Supported by:
Supported by National Natural Science Foundation of China(11301036,11226335,51278065)and Scientific Research Project of Jilin Province Department of Education(No.127 in 2014,No.142 in 2013)

Abstract

Abstract:

In order to obtain the pattern of variation of PM2.5 concentrations in the atmosphere in Beijing City, we build a EEMD-SVR hybrid model that can predict the PM2.5 level in a short term. Firstly, according to the ensemble empirical mode decomposition (EEMD) method to analyse the PM2.5 of Beijing City, the original time series is decomposed into the series of intrinsic mode functions (IMFs) and trend items; then, the periodic variation characteristics of PM2.5 is revealed through the periodic analysis of each intrinsic mode function;finally, we use support vector regression (SVR) to forecast all IMFs and trend items, which reflect the rationality of using SVR model. The results show that the prediction accuracy of mixed EEMD-SVR model is higher than single SVR model.

Key words: ensemble empirical mode decomposition, intrinsic mode functions (IMF), periodicity, support vector regression

CLC Number:

Qin Xiwen, Liu Yuanyuan, Wang Xinmin, Dong Xiaogang, Zhang Yu, Zhou Hongmei. PM2.5 Prediction of Beijing City Based on Ensemble Empirical Mode Decomposition and Support Vector Regression[J].Journal of Jilin University(Earth Science Edition), 2016, 46(2): 563-568.

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PM2.5 Prediction of Beijing City Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

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