中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine

文献类型：期刊论文

作者Du, Zongjuan1; Heng, Jiani2; Niu, Mingfei1; Sun, Shaolong3
刊名ATMOSPHERIC POLLUTION RESEARCH
出版日期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
DOI10.1016/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
WOS记录号WOS:000701178700004
出版者TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
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.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.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。