Air Pollutant Analysis and AQI Prediction Based on GRA and Improved SOA-SVR by Considering COVID-19

Since COVID-19 pneumonia broke out, the Chinese government has taken a series of measures to control the spread of the epidemic, which has made the air quality of Taiyuan in February 2020 significantly better than during the same period in previous years. In this paper, the Gray Relational Analysis...

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Bibliographic Details
Published inAtmosphere Vol. 12; no. 3; p. 336
Main Authors Xu, Ting, Yan, Huichao, Bai, Yanping
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2021
Subjects
Air
air pollutant
Air pollution
Air quality
Algorithms
AQI
Coronaviruses
COVID-19
Disease control
Epidemics
Nitrogen dioxide
Optimization
Optimization algorithms
Outdoor air quality
Pollutant analysis
Pollutants
prediction
Predictions
Regression analysis
Seabirds
Support vector machines
support vector regression (SVR)
China
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Summary:Since COVID-19 pneumonia broke out, the Chinese government has taken a series of measures to control the spread of the epidemic, which has made the air quality of Taiyuan in February 2020 significantly better than during the same period in previous years. In this paper, the Gray Relational Analysis (GRA) method was first applied to evaluate and analyze the influence of six major pollutants on air quality. Then, the improved seagull optimization algorithm (ISOA) was proposed and combined with Support Vector Regression (SVR) to establish a hybrid predicted model ISOA-SVR. Finally, the proposed ISOA-SVR was utilized to predict air quality index (AQI). The experimental results on two kinds of different data showed that the proposed ISOA-SVR had the better generalization ability and robustness compared with other predicted models. Further, the proposed ISOA-SVR is suitable for the prediction of AQI.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos12030336