气候变化领域集成服务门户(CCPortal): Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants

CCPortal

DOI	10.3389/fenvs.2021.761287
	Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants
	Huang, Yanwen; Deng, Yuanchang; Wang, Chen; Fu, Tonglin
通讯作者	Deng, YC ; Wang, C (通讯作者)，Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen, Peoples R China.
发表日期	2021
EISSN	2296-665X
卷号	9
英文摘要	The air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O-3. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to analyze different air pollutants at length, leading to unsatisfactory results. In this study, a model selection forecasting system is proposed that consists of data mining, data analysis, model selection, and multi-objective optimized modules and effectively solves the problems of air pollutants monitoring. The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. And then multiple artificial neural networks are used to forecast the main air pollutants for each category and find the optimal models. Finally, the modified multi-objective optimization algorithm is used to optimize the parameters of optimal models and model selection to obtain final forecasting values from optimal hybrid models. The experiment results of datasets from 13 cities in the Beijing-Tianjin-Hebei Urban Agglomeration demonstrated that the proposed system can simultaneously obtain efficient and reliable data for air quality monitoring.
关键词	POLYCYCLIC AROMATIC-HYDROCARBONS EARLY-WARNING SYSTEM PARTICULATE MATTER QUALITY ASSESSMENT TREND ANALYSIS URBAN AREAS POLLUTION PM2.5 ALGORITHM CLASSIFICATION
英文关键词	air quality index; data analysis; data mining; artificial neural networks; model selection
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000738476900001
来源期刊	FRONTIERS IN ENVIRONMENTAL SCIENCE
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254883
作者单位	[Huang, Yanwen; Deng, Yuanchang; Wang, Chen] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen, Peoples R China; [Fu, Tonglin] LongDong Univ, Sch Math & Stat, Qingyang, Peoples R China; [Fu, Tonglin] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Shapotou Desert Res & Expt Stn, Lanzhou, Peoples R China; [Fu, Tonglin] Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Huang, Yanwen,Deng, Yuanchang,Wang, Chen,et al. Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants[J]. 中国科学院西北生态环境资源研究院,2021,9.
APA	Huang, Yanwen,Deng, Yuanchang,Wang, Chen,&Fu, Tonglin.(2021).Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants.FRONTIERS IN ENVIRONMENTAL SCIENCE,9.
MLA	Huang, Yanwen,et al."Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants".FRONTIERS IN ENVIRONMENTAL SCIENCE 9(2021).