PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environ...

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Autores principales:	Qingtian Zeng, Chao Wang, Geng Chen, Hua Duan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/0989b27f8946454fbb04cbec5edbced8
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id	oai:doaj.org-article:0989b27f8946454fbb04cbec5edbced8
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spelling	oai:doaj.org-article:0989b27f8946454fbb04cbec5edbced82021-11-15T01:19:37ZPM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks1530-867710.1155/2021/7000986https://doaj.org/article/0989b27f8946454fbb04cbec5edbced82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7000986https://doaj.org/toc/1530-8677Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM-STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM-STGCN model can more fully capture the spatial-temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%.Qingtian ZengChao WangGeng ChenHua DuanHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Qingtian Zeng Chao Wang Geng Chen Hua Duan PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
description	Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM-STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM-STGCN model can more fully capture the spatial-temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%.
format	article
author	Qingtian Zeng Chao Wang Geng Chen Hua Duan
author_facet	Qingtian Zeng Chao Wang Geng Chen Hua Duan
author_sort	Qingtian Zeng
title	PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
title_short	PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
title_full	PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
title_fullStr	PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
title_full_unstemmed	PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks
title_sort	pm2.5 concentration forecasting in industrial parks based on attention mechanism spatiotemporal graph convolutional networks
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/0989b27f8946454fbb04cbec5edbced8
work_keys_str_mv	AT qingtianzeng pm25concentrationforecastinginindustrialparksbasedonattentionmechanismspatiotemporalgraphconvolutionalnetworks AT chaowang pm25concentrationforecastinginindustrialparksbasedonattentionmechanismspatiotemporalgraphconvolutionalnetworks AT gengchen pm25concentrationforecastinginindustrialparksbasedonattentionmechanismspatiotemporalgraphconvolutionalnetworks AT huaduan pm25concentrationforecastinginindustrialparksbasedonattentionmechanismspatiotemporalgraphconvolutionalnetworks
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PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks

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