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Prediction of daily PM2.5 concentration in China using data-driven ordinary differential equations

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  • Wang, Yufang
  • Wang, Haiyan
  • Zhang, Shuhua

Abstract

Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a daily prediction method of PM2.5 concentration by using data-driven ordinary differential equation (ODE) models. Specifically, based on the historical PM2.5 concentration, this method combines genetic programming and orthogonal least square method to evolve the ODE models, which describe the transport of PM2.5 and then uses the data-driven ODEs to predict the air quality in the future. Experiment results show that the ODE models obtain similar prediction results as the typical statistical model, and the prediction results from this method are relatively good. To our knowledge, this is the first attempt to evolve data-driven ODE models to study PM2.5 prediction.

Suggested Citation

  • Wang, Yufang & Wang, Haiyan & Zhang, Shuhua, 2020. "Prediction of daily PM2.5 concentration in China using data-driven ordinary differential equations," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:apmaco:v:375:y:2020:i:c:s0096300320300576
    DOI: 10.1016/j.amc.2020.125088
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    References listed on IDEAS

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    1. Yufang Wang & Haiyan Wang & Shuhua Chang & Adrian Avram, 2018. "Prediction of daily PM2.5 concentration in China using partial differential equations," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    2. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
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