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Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method
Jiang, Fuxin1,2; Zhang, Chengyuan3; Sun, Shaolong4; Sun, Jingyun5
2021-12-01
Source PublicationAPPLIED SOFT COMPUTING
ISSN1568-4946
Volume113Pages:15
AbstractFor hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modeling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the statistics-based models, traditional machine learning models, the popular deep learning models and several existing hybrid models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations. (C) 2021 Elsevier B.V. All rights reserved.
KeywordPM2.5 concentration forecasting Complete ensemble empirical mode decomposition with adaptive noise Temporal convolutional Data patterns Deep learning
DOI10.1016/j.asoc.2021.107988
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities, China[SK2021007] ; Innovation Ability Improvement Project in Colleges and Universities of Gansu Province of China[2019A-060]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000765558100001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/60158
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhang, Chengyuan; Sun, Shaolong
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.Xidian Univ, Sch Econ & Management, Xian 710126, Peoples R China
4.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
5.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou 730020, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Fuxin,Zhang, Chengyuan,Sun, Shaolong,et al. Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method[J]. APPLIED SOFT COMPUTING,2021,113:15.
APA Jiang, Fuxin,Zhang, Chengyuan,Sun, Shaolong,&Sun, Jingyun.(2021).Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method.APPLIED SOFT COMPUTING,113,15.
MLA Jiang, Fuxin,et al."Forecasting hourly PM2.5 based on deep temporal convolutional neural network and decomposition method".APPLIED SOFT COMPUTING 113(2021):15.
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