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Applications of SVR-PSO Model and Multivariate Linear Regression Model in PM2.5 Concentration Forecasting

Author

Listed:
  • Guo-Feng Fan

    (School of Mathematics &Statistics Science, Ping Ding Shan University, Ping Ding, China)

  • Meng-Qi Liang

    (School of Mathematics &Statistics Science, Ping Ding Shan University, Ping Ding, China)

  • Jing-Ru Li

    (School of Economics and Management, Ping Ding Shan University, Ping Ding, China)

  • Wen-Lu Ma

    (School of Economics and Management, Ping Ding Shan University, Ping Ding, China)

Abstract

At present, the fog and haze problem is intensified, which has a great impact on the production of enterprises and living of the residents. PM2.5 is an important indicator of air pollution and it also receives much concern. This article collects the reliable data of PM2.5 in the five industrial cities in Henan Province from Weather Report Network, and PM2.5 Data Network since 2015. The effective approaches to forecast PM2.5 concentration is proposed, i.e., the improved multivariate linear regression (namely IMLR) model and support vector regression with particle swarm optimization algorithm (namely SVR-PSO) model. The empirical results demonstrate that the proposed IMLR and SVR-PSO forecasting models are effective, and also, could be an instructive reference for weather quality forecasting, safe travel, and safe production.

Suggested Citation

  • Guo-Feng Fan & Meng-Qi Liang & Jing-Ru Li & Wen-Lu Ma, 2017. "Applications of SVR-PSO Model and Multivariate Linear Regression Model in PM2.5 Concentration Forecasting," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 8(4), pages 53-69, October.
    • Handle: RePEc:igg:jaec00:v:8:y:2017:i:4:p:53-69
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