Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting

doi:10.1016/j.jenvman.2017.02.071

CSpace

	Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting
	Niu, Mingfei 1; Gan, Kai 1; Sun, Shaolong 2; Li, Fengying 3
	2017-07-01
发表期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
卷号	196 页码:110-118
摘要	PM2.5 concentration have received considerable attention from meteorologists, who are able to notify the public and take precautionary measures to prevent negative effects on health. Therefore, establishing an efficient early warning system plays a critical role in fostering public health in heavily polluted areas. In this study, ensemble empirical mode decomposition and least square support vector machine (EEMD-LSSVM) based on Phase space reconstruction (PSR) is proposed for day-ahead PM2.5 concentration prediction, according to the application of a decomposition-ensemble learning paradigm. The main methods of the proposed model mainly include: first, EEMD is presented to decompose the original data of PM2.5 concentration into some intrinsic model functions (IMFs); second, PSR is applied to determine the input form of each extracted component; third, LSSVM, an effective forecasting tool, is used to predict all reconstructed components independently; finally, another LSSVM is employed to aggregate all predicted components into ensemble results for the final prediction. The empirical results show that this proposed model can outperform the comparison models and can significantly improve the prediction performance in terms of higher predictive and directional accuracy. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	PM2.5 concentration forecasting Decomposition-ensemble learning paradigm EEMD PSR LSSVM
DOI	10.1016/j.jenvman.2017.02.071
语种	英语
资助项目	National Natural Science Foundation of China[71501176] ; China Postdoctoral Science Foundation[2015M580141] ; National Natural Science Foundation of Ningxia[NZ15258]
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000401888300013
出版者	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/25489
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, Inst Syst Sci, Beijing 100190, Peoples R China 3.Ningxia Normal Univ, Sch Math & Comp Sci, Guyuan 756000, Peoples R China
推荐引用方式 GB/T 7714	Niu, Mingfei,Gan, Kai,Sun, Shaolong,et al. Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2017,196:110-118.
APA	Niu, Mingfei,Gan, Kai,Sun, Shaolong,&Li, Fengying.(2017).Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting.JOURNAL OF ENVIRONMENTAL MANAGEMENT,196,110-118.
MLA	Niu, Mingfei,et al."Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting".JOURNAL OF ENVIRONMENTAL MANAGEMENT 196(2017):110-118.