吉林大学学报(地球科学版) ›› 2016, Vol. 46 ›› Issue (2): 563-568.doi: 10.13278/j.cnki.jjuese.201602206

• 地质工程与环境工程 • 上一篇    下一篇

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

秦喜文1,2,3, 刘媛媛2, 王新民2, 董小刚2, 张瑜2, 周红梅2   

  1. 1. 长春工业大学研究生院, 长春 130012;
    2. 长春工业大学基础科学学院, 长春 130012;
    3. 长春工业大学汽车工程研究院, 长春 130012
  • 收稿日期:2015-07-01 发布日期:2016-03-26
  • 通讯作者: 王新民(1957-),男,教授,主要从事数值分析方面的研究,E-mail:wxm@jlu.edu.cn E-mail:wxm@jlu.edu.cn
  • 作者简介:秦喜文(1979-),男,副教授,主要从事HHT理论与应用方面的研究,E-mail:qinxiwen@ccut.edu.cn
  • 基金资助:

    国家自然科学基金项目(11301036,11226335,51278065);吉林省教育厅科研项目(2014第127号,2013第142号)

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

Qin Xiwen1,2,3, Liu Yuanyuan2, Wang Xinmin2, Dong Xiaogang2, Zhang Yu2, Zhou Hongmei2   

  1. 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)

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摘要：

为了更好地掌握大气中PM2.5浓度的变化规律,利用EEMD-SVR混合模型对该地区的PM2.5浓度值进行了短期预测。首先,通过采用整体经验模态分解(EEMD)方法分析北京市PM2.5,把原始时间序列分解成多个固有模态函数和趋势项;然后,对各阶固有模态函数进行周期性分析,揭示了北京市PM2.5的周期性变化特点;最后,对经过EEMD分解后的各阶固有模态函数和趋势项用支持向量机回归(SVR)方法进行预测。结果表明, EEMD-SVR混合模型比单一的SVR模型预测精度更高。

关键词: 整体经验模态分解, 固有模态函数, 周期性, 支持向量机回归

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

中图分类号: 

  • C81

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