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

doi:10.1016/j.apr.2021.101153

CSpace

	An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine
	Du, Zongjuan 1; Heng, Jiani 2; Niu, Mingfei 1; Sun, Shaolong 3
	2021-09-01
发表期刊	ATMOSPHERIC POLLUTION RESEARCH
ISSN	1309-1042
卷号	12 期号:9 页码:15
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.
关键词	Air quality early-warning system Length-changeable incremental extreme learning machine Hybrid ensemble model Fuzzy evaluation
DOI	10.1016/j.apr.2021.101153
收录类别	SCI
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[xpt012020022]
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000701178700004
出版者	TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59315
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.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
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.