An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine

doi:10.1016/j.apr.2021.101153

	An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine
	Du, Zongjuan 3; Heng, Jiani 1; Niu, Mingfei 3; Sun, Shaolong 2
刊名	ATMOSPHERIC POLLUTION RESEARCH
	2021-09-01
卷号	12 期号:9 页码:15
关键词	Air quality early-warning system Length-changeable incremental extreme learning machine Hybrid ensemble model Fuzzy evaluation
ISSN号	1309-1042
DOI	10.1016/j.apr.2021.101153
英文摘要	Air 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.
资助项目	Fundamental Research Funds for the Central Universities[xpt012020022]
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
WOS记录号	WOS:000701178700004
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59314]
专题	中国科学院数学与系统科学研究院
通讯作者	Sun, Shaolong
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China 3.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China
推荐引用方式 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.