CSpace
An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine
Du, Zongjuan1; Heng, Jiani2; Niu, Mingfei1; Sun, Shaolong3
2021-09-01
Source PublicationATMOSPHERIC POLLUTION RESEARCH
ISSN1309-1042
Volume12Issue:9Pages:15
AbstractAir pollution has lots of adverse effects on industrial production and public life. Thus, it is an urgent task to construct an efficient air quality early-warning system to guide public life and production. This paper proposes an innovative air pollution early-warning system, including four main modules: clustering, preprocessing, forecasting and evaluation. In the clustering module, with the aim of building an efficient air pollution warning system, the air pollution situation of 31 provincial capitals is clustered and the study areas of the current study are selected based on the clustering result. A new data preprocessing algorithm is conducted to excavate the potential characteristics of the raw time series in the first place in the preprocessing module. Then, the lengthchangeable incremental extreme learning machine is used to forecast each component. In the evaluation module, the air quality is qualitatively analyzed by the fuzzy evaluation method. Moreover, the DM test and the SPA test are employed to test the accuracy of the forecasting model. The experimental results of eighteen data sets from three cities show that the hybrid air quality early-warning system establish in the study not only has higher accuracy and generalization ability than other benchmark models, but can provide sufficient air quality information, which is essential to control air pollution.
KeywordAir quality early-warning system Length-changeable incremental extreme learning machine Hybrid ensemble model Fuzzy evaluation
DOI10.1016/j.apr.2021.101153
Indexed BySCI
Language英语
Funding ProjectFundamental Research Funds for the Central Universities[xpt012020022]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000701178700004
PublisherTURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59315
Collection中国科学院数学与系统科学研究院
Corresponding AuthorSun, Shaolong
Affiliation1.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
Recommended Citation
GB/T 7714
Du, Zongjuan,Heng, Jiani,Niu, Mingfei,et al. An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine[J]. ATMOSPHERIC POLLUTION RESEARCH,2021,12(9):15.
APA Du, Zongjuan,Heng, Jiani,Niu, Mingfei,&Sun, Shaolong.(2021).An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine.ATMOSPHERIC POLLUTION RESEARCH,12(9),15.
MLA Du, Zongjuan,et al."An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine".ATMOSPHERIC POLLUTION RESEARCH 12.9(2021):15.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Du, Zongjuan]'s Articles
[Heng, Jiani]'s Articles
[Niu, Mingfei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Du, Zongjuan]'s Articles
[Heng, Jiani]'s Articles
[Niu, Mingfei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Du, Zongjuan]'s Articles
[Heng, Jiani]'s Articles
[Niu, Mingfei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.