On hourly prediction of PM2.5 using spatial–temporal graph convolutional network

Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM 2.5 hourly prediction is an essential instrument for air protection because it can be utilized in developing air pollution prevention and control techniques and provides an early warning...

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Veröffentlicht in:	Earth science informatics Jg. 18; H. 2; S. 402
Hauptverfasser:	Ren, Zhenxing, Ji, Xinxin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Schlagworte:	Air pollution Artificial neural networks Decomposition Early warning systems Earth and Environmental Science Earth Sciences Earth System Sciences Forecasting Forecasting models Information Systems Applications (incl.Internet) Meteorological data Ontology Particulate matter Permutations Pollution prevention Prediction models Search algorithms Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Beijing China China Spatial–temporal graph convolutional network Attention mechanism Hourly prediction of air pollutants Empirical mode decomposition
ISSN:	1865-0473, 1865-0481
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Abstract	Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM 2.5 hourly prediction is an essential instrument for air protection because it can be utilized in developing air pollution prevention and control techniques and provides an early warning system for dangerous air pollutants. Conventional forecasting models are limited due to the extraction of spatial and spatiotemporal features between variables, emphasizing more on the temporal characteristics of the variables. Considering the spatiotemporal correlation of air pollution, we propose a novel hourly prediction model ICEEMDAN-SSA-VMD-ABSTGCN. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) approach is utilized for the decomposition of observed PM 2.5 concentrations. Variational mode decomposition (VMD) is then leveraged to further decompose the selected signals with the highest multi-scale permutation entropy (MPE) after its parameters have been optimized using the sparrow search algorithm (SSA). Next, an attention-based spatial–temporal graph convolutional network (ABSTGCN) was built by merging several effect factors, such as air pollution factors, and meteorological data. This methodology can be used for concentration forecasting of PM 2.5 in the future.
AbstractList	Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM 2.5 hourly prediction is an essential instrument for air protection because it can be utilized in developing air pollution prevention and control techniques and provides an early warning system for dangerous air pollutants. Conventional forecasting models are limited due to the extraction of spatial and spatiotemporal features between variables, emphasizing more on the temporal characteristics of the variables. Considering the spatiotemporal correlation of air pollution, we propose a novel hourly prediction model ICEEMDAN-SSA-VMD-ABSTGCN. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) approach is utilized for the decomposition of observed PM 2.5 concentrations. Variational mode decomposition (VMD) is then leveraged to further decompose the selected signals with the highest multi-scale permutation entropy (MPE) after its parameters have been optimized using the sparrow search algorithm (SSA). Next, an attention-based spatial–temporal graph convolutional network (ABSTGCN) was built by merging several effect factors, such as air pollution factors, and meteorological data. This methodology can be used for concentration forecasting of PM 2.5 in the future. Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM2.5 hourly prediction is an essential instrument for air protection because it can be utilized in developing air pollution prevention and control techniques and provides an early warning system for dangerous air pollutants. Conventional forecasting models are limited due to the extraction of spatial and spatiotemporal features between variables, emphasizing more on the temporal characteristics of the variables. Considering the spatiotemporal correlation of air pollution, we propose a novel hourly prediction model ICEEMDAN-SSA-VMD-ABSTGCN. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) approach is utilized for the decomposition of observed PM2.5 concentrations. Variational mode decomposition (VMD) is then leveraged to further decompose the selected signals with the highest multi-scale permutation entropy (MPE) after its parameters have been optimized using the sparrow search algorithm (SSA). Next, an attention-based spatial–temporal graph convolutional network (ABSTGCN) was built by merging several effect factors, such as air pollution factors, and meteorological data. This methodology can be used for concentration forecasting of PM2.5 in the future.
ArticleNumber	402
Author	Ren, Zhenxing Ji, Xinxin
Author_xml	– sequence: 1 givenname: Zhenxing surname: Ren fullname: Ren, Zhenxing email: renzhenxing@tyut.edu.cn, renzhenxing@tyut.edu.cn organization: College of Artificial Intelligence, Taiyuan University of Technology – sequence: 2 givenname: Xinxin surname: Ji fullname: Ji, Xinxin organization: Engineering Design Group Co., Ltd, Zhejiang University of Technology
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Snippet	Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM 2.5 hourly prediction is an essential... Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM2.5 hourly prediction is an essential...
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SubjectTerms	Air pollution Artificial neural networks Decomposition Early warning systems Earth and Environmental Science Earth Sciences Earth System Sciences Forecasting Forecasting models Information Systems Applications (incl.Internet) Meteorological data Ontology Particulate matter Permutations Pollution prevention Prediction models Search algorithms Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics
Title	On hourly prediction of PM2.5 using spatial–temporal graph convolutional network
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