Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting
文献类型：期刊论文
| 作者 | Niu, Mingfei1; Gan, Kai1; Sun, Shaolong2; Li, Fengying3 |
| 刊名 | JOURNAL OF ENVIRONMENTAL MANAGEMENT
 |
| 出版日期 | 2017-07-01 |
| 卷号 | 196页码:110-118 |
| 关键词 | PM2.5 concentration forecasting Decomposition-ensemble learning paradigm EEMD PSR LSSVM |
| ISSN号 | 0301-4797 |
| DOI | 10.1016/j.jenvman.2017.02.071 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000401888300013 |
| 出版者 | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/25488]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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