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山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (6): 76-83.doi: 10.6040/j.issn.1672-3961.0.2014.214

• 土木工程 • 上一篇    下一篇

基于气象因素的PM2.5质量浓度预测模型

刘杰1, 杨鹏2, 吕文生1, 刘阿古达木1, 刘俊秀2   

  1. 1. 北京科技大学土木与环境工程学院, 北京 100083;
    2. 北京市信息服务工程重点实验室(北京联合大学), 北京 100101
  • 收稿日期:2014-08-05 修回日期:2015-06-02 出版日期:2015-12-20 发布日期:2014-08-05
  • 通讯作者: 杨鹏(1965-),男,四川大英人,教授,博士,博导,主要研究方向为矿业工程,安全工程与系统工程研究.E-mail:yangpeng@buu.edu.cn E-mail:yangpeng@buu.edu.cn
  • 作者简介:刘杰(1986-),男,云南禄丰人,博士研究生,主要研究方向为城市空气质量监测,矿井通风与安全.E-mail:liujie19860809@qq.com
  • 基金资助:
    北京市属高等学校高层次人才引进与培养——"长城学者"培养计划资助项目(CIT&TCD20130320)

Prediction models of PM2.5 mass concentration based on meteorological factors

LIU Jie1, YANG Peng2, LYU Wensheng1, LIU Agudamu1, LIU Junxiu2   

  1. 1. School of Civil and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
  • Received:2014-08-05 Revised:2015-06-02 Online:2015-12-20 Published:2014-08-05

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摘要： 为得出拟合效果最佳的预测模型,建立了多元回归和机器学习预测模型对PM2.5质量浓度进行预测。在输入气象因素的基础上,引入污染物质量浓度基础值和周期因素两类变量作为预测输入,并对4种预测模型进行对比研究。研究结果表明:对预测输入进行改进后,多元线性回归预测模型拟合优度由0.52提高至0.64,所选取的气象参数、污染物质量浓度基础值和周期因素能较好地描述PM2.5质量浓度的日变化情况;与多元线性回归预测模型相比,BP神经网络和支持向量机两种预测模型能较好地捕捉PM2.5质量浓度与预测输入之间的非线性影响规律,整体拟合优度分别达0.69和0.74,预测准确度较高;支持向量机预测模型可作为PM2.5质量浓度预测的首选方法。

关键词: 机器学习, BP神经网络, 支持向量机, 多元线性回归, PM2.5

Abstract: In order to get the optimal prediction model, the prediction models of PM2.5 mass concentration based on multiple linear regression and machine learning were developed. Basic values of pollutants mass concentrations and periodical factors were introduced as predictive inputs based on meteorological factors. Then four prediction models were developed for comparison. Results showed that goodness of fit of multiple linear regression model based on improved predictive inputs was increased from 0.52 to 0.64. The selected meteorological factors, basic values of pollutants mass concentrations and periodical factors could accurately describe daily variation of PM2.5. BP neural network and support vector machine models could be trained to model the highly non-linear relationships between PM2.5 mass concentration and predictive inputs. They provided satisfactory results with goodness of fit of 0.69 and 0.74, respectively. Support vector machine model was proved to be optimal prediction model of PM2.5 mass concentration.

Key words: PM2.5, multiple linear regression, support vector machine, machine learning, BP neural network

中图分类号: 

  • X831
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