Residential College | false |
Status | 已發表Published |
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 Publication | Scientific Reports |
ISSN | 2045-2322 |
Volume | 12Issue: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. |
DOI | 10.1038/s41598-022-09344-0 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Science & Technology - Other Topics |
WOS Subject | Multidisciplinary Sciences |
WOS ID | WOS:000780164200091 |
Publisher | NATURE PORTFOLIO, HEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY |
Scopus ID | 2-s2.0-85127691558 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Xinyang Wang; Weiwei Lin |
Affiliation | 1.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. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment