中文版 | English
Title

A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach

Author
Corresponding AuthorSun, Shaolong
Publication Years
2022-07-01
DOI
Source Title
ISSN
0277-6693
EISSN
1099-131X
Abstract

A reliable and efficient forecasting system can be used to warn the general public against the increasing PM2.5 concentration. This paper proposes a novel AdaBoost-ensemble technique based on a hybrid data preprocessing-analysis strategy, with the following contributions: (i) a new decomposition strategy is proposed based on the hybrid data preprocessing-analysis strategy, which combines the merits of two popular decomposition algorithms and has been proven to be a promising decomposition strategy; (ii) the long short-term memory (LSTM), as a powerful deep learning forecasting algorithm, is applied to individually forecast the decomposed components, which can effectively capture the long-short patterns of complex time series; and (iii) a novel AdaBoost-LSTM ensemble technique is then developed to integrate the individual forecasting results into the final forecasting results, which provides significant improvement to the forecasting performance. To evaluate the proposed model, a comprehensive and scientific assessment system with several evaluation criteria, comparison models, and experiments is designed. The experimental results indicate that our developed hybrid model considerably surpasses the compared models in terms of forecasting precision and statistical testing and that its excellent forecasting performance can guide in developing effective control measures to decrease environmental contamination and prevent the health issues caused by a high PM2.5 concentration.

Keywords
URL[Source Record]
Indexed By
SSCI ; EI
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China[
WOS Research Area
Business & Economics
WOS Subject
Economics ; Management
WOS Accession No
WOS:000831065900001
Publisher
EI Accession Number
20223112467467
EI Keywords
Adaptive Boosting ; Forecasting ; Learning Systems ; Time Series Analysis
ESI Classification Code
Computer Software, Data HAndling And Applications:723 ; Mathematical Statistics:922.2
ESI Research Field
ECONOMICS BUSINESS
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:2
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/364982
DepartmentSchool of Business
Affiliation
1.Sun Yat Sen Univ, Sch Business, Guangzhou, Peoples R China
2.Southern Univ Sci & Technol, Sch Business, Shenzhen, Peoples R China
3.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
6.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China
First Author AffilicationSchool of Business
Recommended Citation
GB/T 7714
Li, Zhongfei,Gan, Kai,Sun, Shaolong,et al. A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach[J]. JOURNAL OF FORECASTING,2022.
APA
Li, Zhongfei,Gan, Kai,Sun, Shaolong,&Wang, Shouyang.(2022).A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach.JOURNAL OF FORECASTING.
MLA
Li, Zhongfei,et al."A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach".JOURNAL OF FORECASTING (2022).
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A new PM2 5 concentr(3647KB) Restricted Access--
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