A new PM2.5 concentration forecasting system based on AdaBoost-ensemble system with deep learning approach
文献类型：期刊论文
| 作者 | Li, Zhongfei1,2; Gan, Kai1; Sun, Shaolong3; Wang, Shouyang4,5,6 |
| 刊名 | JOURNAL OF FORECASTING
 |
| 出版日期 | 2022-07-28 |
| 页码 | 22 |
| ISSN号 | 0277-6693 |
| 关键词 | AdaBoost-ensemble deep learning hybrid data preprocessing-analysis strategy LSTM |
| DOI | 10.1002/for.2883 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[71991474] ; National Natural Science Foundation of China[71721001] ; National Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007] |
| WOS研究方向 | Business & Economics |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:000831065900001 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61175]  |
| 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.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 |
| 推荐引用方式 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,2022:22. |
| 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,22. |
| 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):22. |
入库方式： OAI收割
来源：数学与系统科学研究院
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