Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach
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 beco...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2019/1279565 |
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| _version_ | 1832554588009398272 |
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| author | Jihan Li Xiaoli Li Kang Wang |
| author_facet | Jihan Li Xiaoli Li Kang Wang |
| author_sort | Jihan Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ac98fed0da1149d58efa40df5737cad7 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-ac98fed0da1149d58efa40df5737cad72025-02-03T05:51:07ZengWileyAdvances in Meteorology1687-93091687-93172019-01-01201910.1155/2019/12795651279565Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model ApproachJihan Li0Xiaoli Li1Kang Wang2Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaUrbanization, 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.http://dx.doi.org/10.1155/2019/1279565 |
| spellingShingle | Jihan Li Xiaoli Li Kang Wang Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach Advances in Meteorology |
| title | Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach |
| title_full | Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach |
| title_fullStr | Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach |
| title_full_unstemmed | Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach |
| title_short | Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach |
| title_sort | atmospheric pm2 5 concentration prediction based on time series and interactive multiple model approach |
| url | http://dx.doi.org/10.1155/2019/1279565 |
| work_keys_str_mv | AT jihanli atmosphericpm25concentrationpredictionbasedontimeseriesandinteractivemultiplemodelapproach AT xiaolili atmosphericpm25concentrationpredictionbasedontimeseriesandinteractivemultiplemodelapproach AT kangwang atmosphericpm25concentrationpredictionbasedontimeseriesandinteractivemultiplemodelapproach |