Title | A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach |
Author | |
Corresponding Author | Sun, Shaolong |
Publication Years | 2022-07-01
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DOI | |
Source Title | |
ISSN | 0277-6693
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EISSN | 1099-131X
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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 | |
Language | English
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SUSTech Authorship | Others
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Funding Project | National Natural Science Foundation of China[
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WOS Research Area | Business & Economics
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WOS Subject | Economics
; Management
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WOS Accession No | WOS:000831065900001
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Publisher | |
EI Accession Number | 20223112467467
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EI Keywords | Adaptive Boosting
; Forecasting
; Learning Systems
; Time Series Analysis
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ESI Classification Code | Computer Software, Data HAndling And Applications:723
; Mathematical Statistics:922.2
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ESI Research Field | ECONOMICS BUSINESS
|
Data Source | Web of Science
|
Citation statistics |
Cited Times [WOS]:2
|
Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/364982 |
Department | School 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 Affilication | School 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.
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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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File Name/Size | DocType | Version | Access | License | ||
A new PM2 5 concentr(3647KB) | Restricted Access | -- |
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