Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach
Joint Authors
Li, Xiao-Li
Li, Jihan
Wang, Kang
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years.
Air pollution has attracted more and more attention.
However, PM2.5 is the main particulate matter in air pollution.
Therefore, how to predict PM2.5 accurately and effectively has become a concern of experts and scholars.
For the problem, atmosphere PM2.5 concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper.
PM2.5 concentration is collected by using the monitor at different air quality levels.
The time series models are established by historical PM2.5 concentration data, which were given by the autoregressive model (AR).
In the paper, three PM2.5 time series models are established for three different air quality levels.
Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches.
Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm.
It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5 prediction, and it is effective.
American Psychological Association (APA)
Li, Jihan& Li, Xiao-Li& Wang, Kang. 2019. Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances in Meteorology،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1118517
Modern Language Association (MLA)
Li, Jihan…[et al.]. Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances in Meteorology No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1118517
American Medical Association (AMA)
Li, Jihan& Li, Xiao-Li& Wang, Kang. Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach. Advances in Meteorology. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1118517
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118517