兰州大学机构库 >公共卫生学院
CEEMD-subset-OASVR-GRNN for ozone forecasting: Xiamen and Harbin as cases
Zhu, SL(朱素玲)1; Wang, Xiao2; Shi, Naiyu3; Lu, Mingming4
2020-04
Source PublicationATMOSPHERIC POLLUTION RESEARCH
ISSN1309-1042
Volume11Issue:4
AbstractTropospheric ozone is a standard air pollutant, and can adversely affect human respiratory system. Many metropolitan areas around the world struggle to meet ozone standards. Therefore, timely and effective ozone prediction can help regulatory agencies to prevent the harm to human body and environment induced by excessive ozone concentration in advance. For selecting the optimal individual model set in combined forecasting, this research proposes CEEMD-Subset-OASVR-GRNN model, based on complete ensemble empirical mode decomposition (CEEMD) method, support vector regression (SVR), generalized regression neural network (GRNN) and optimization algorithms (OA), to predict the daily average concentration of ozone. Specifically, for the ozone time series, CEEMD is used to decompose the original data into three intrinsic mode functions (IMFs), PSO-SVR, PSOGSA-SVR, GWO-SVR and GRNN are employed to model and predict the IMFs, and the prediction results are randomly combined to establish 100 individual models (appendix Table A1). The selection methods of the individual models include MSE ranking, factor score and unsupervised learning systematic clustering, and the influence of number of individual models on combined forecasting is studied. The ozone series of two very distinct Chinese metropolitan areas, Xiamen and Harbin, are selected as the experimental data. The prediction results show that the systematic clustering method is helpful for effectively improving the prediction accuracy of the combined model.
KeywordCombined forecasting Model selection Number of individual models Ozone prediction
DOI10.1016/j.apr.2020.01.003
Indexed BySCIE ; SSCI
Language英语
Funding ProjectNational Bureau of Statistic of China[2018LZ30] ; Fundamental Research Funds for the Central Universities[lzujbky-2018-65]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000520028700011
PublisherTURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP
Original Document TypeArticle
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/421739
Collection公共卫生学院
Corresponding AuthorZhu, Suling; Wang, Xiao
Affiliation1.Lanzhou Univ, Sch Publ Hlth, Lanzhou, Gansu, Peoples R China
2.Lanzhou Univ, Sch Math & Stat, Lanzhou, Gansu, Peoples R China
3.Lanzhou Univ, Sch Hosp Stomatol, Lanzhou, Gansu, Peoples R China
4.Univ Cincinnati, Dept Chem & Environm Engn, Cincinnati, OH USA
First Author AffilicationSchool of Public health
Corresponding Author AffilicationSchool of Public health;  School of Mathematics and Statistics
Recommended Citation
GB/T 7714
Zhu, Suling,Wang, Xiao,Shi, Naiyu,et al. CEEMD-subset-OASVR-GRNN for ozone forecasting: Xiamen and Harbin as cases[J]. ATMOSPHERIC POLLUTION RESEARCH,2020,11(4).
APA Zhu, Suling,Wang, Xiao,Shi, Naiyu,&Lu, Mingming.(2020).CEEMD-subset-OASVR-GRNN for ozone forecasting: Xiamen and Harbin as cases.ATMOSPHERIC POLLUTION RESEARCH,11(4).
MLA Zhu, Suling,et al."CEEMD-subset-OASVR-GRNN for ozone forecasting: Xiamen and Harbin as cases".ATMOSPHERIC POLLUTION RESEARCH 11.4(2020).
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