Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (3): 51-57.doi: 10.16180/j.cnki.issn1007-7820.2022.03.008

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Prediction of PM2.5 Based on External Influences and Time-Series Factors

Yanmei YANG,Zongmao CHENG   

  1. School of Sciences,Hangzhou Dianzi University,Hangzhou 310037,China
  • Received:2020-10-23 Online:2022-03-15 Published:2022-04-02
  • Supported by:
    National Natural Science Foundation of China(61370087);National Natural Science Foundation of China(71802065)


As the haze problem gradually worsens, the prediction of one of its main component PM2.5 has become a widespread concern. The daily concentration of PM2.5 is affected by many factors, and it has the characteristics of non-linear and time-varying, which is difficult to accurately predict.To solve this problem, a prediction method of PM2.5 daily concentration based on external influences and time-series factors is proposed. With this method, the main external factors and time factors of PM2.5 daily concentration are separated, and the BP neural network preliminary prediction model based on the main external factors and the combined residual correction model of EEMD-LSTM neural network based on time factor are established. The daily PM2.5 concentration and other related factors data of Hangzhou from 2014 to 2019 are used for simulation experiments. The results show that compared with other models, the root mean square error of the prediction model proposed in the study is 2.74, and the prediction accuracy is higher.

Key words: haze, PM2.5, BP, EEMD, LSTM, time series prediction, neural network, time series decompose, combination prediction

CLC Number: 

  • TP39