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An ambient air quality evaluation model based on improved evidence theory
Qiao Sun1,2; Tong Zhang1,2; Xinyang Wang1,2; Weiwei Lin3; Simon Fong4; Zhibo Chen1,2; Fu Xu1,2; Ling Wu1
2022-04-06
Source PublicationScientific Reports
ISSN2045-2322
Volume12Issue:1Pages:5753
Abstract

It is significant to evaluate the air quality scientifically for the management of air pollution. As an air quality comprehensive evaluation problem, its uncertainty can be effectively addressed by the Dempster–Shafer (D–S) evidence theory. However, there is not enough research on air quality comprehensive assessment using D–S theory. Aiming at the counterintuitive fusion results of the D–S combination rule in the field of comprehensive decision, an improved evidence theory with evidence weight and evidence decision credibility (here namely DCre-Weight method) is proposed, and it is used to comprehensively evaluate air quality. First, this method determines the weights of evidence by the entropy weight method and introduces the decision credibility by calculating the dispersion of different evidence decisions. An algorithm case shows that the credibility of fusion results is improved and the uncertainty is well expressed. It can make reasonable fusion results and solve the problems of D–S. Then, the air quality evaluation model based on improved evidence theory (here namely the DCreWeight model) is proposed. Finally, according to the hourly air pollution data in Xi’an from June 1, 2014, to May 1, 2016, comparisons are made with the D–S, other improved methods of evidence theory, and a recent fuzzy synthetic evaluation method to validate the effectiveness of the model. Under the national AQCI standard, the MAE and RMSE of the DCreWeight model are 1.02 and 1.17. Under the national AQI standard, the DCreWeight model has the minimal MAE, RMSE, and maximal index of agreement, which validated the superiority of the DCreWeight model. Therefore, the DCreWeight model can comprehensively evaluate air quality. It can provide a scientific basis for relevant departments to prevent and control air pollution.

DOI10.1038/s41598-022-09344-0
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000780164200091
PublisherNATURE PORTFOLIO, HEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY
Scopus ID2-s2.0-85127691558
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Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorXinyang Wang; Weiwei Lin
Affiliation1.School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China
2.Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing, 100083, China
3.School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
4.Department of Computer and Information Science, University of Macau, Taipa, Macao
Recommended Citation
GB/T 7714
Qiao Sun,Tong Zhang,Xinyang Wang,et al. An ambient air quality evaluation model based on improved evidence theory[J]. Scientific Reports, 2022, 12(1), 5753.
APA Qiao Sun., Tong Zhang., Xinyang Wang., Weiwei Lin., Simon Fong., Zhibo Chen., Fu Xu., & Ling Wu (2022). An ambient air quality evaluation model based on improved evidence theory. Scientific Reports, 12(1), 5753.
MLA Qiao Sun,et al."An ambient air quality evaluation model based on improved evidence theory".Scientific Reports 12.1(2022):5753.
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