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

Yufang Wang, Haiyan Wang, Shuhua Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number125088
JournalApplied Mathematics and Computation
Volume375
DOIs
StatePublished - Jun 15 2020

Keywords

  • Concentration data
  • Genetic programming
  • Least square method
  • ODE models
  • PM prediction

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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