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Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction
The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955989/ https://www.ncbi.nlm.nih.gov/pubmed/36832613 http://dx.doi.org/10.3390/e25020247 |
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author | Jin, Xue-Bo Wang, Zhong-Yao Kong, Jian-Lei Bai, Yu-Ting Su, Ting-Li Ma, Hui-Jun Chakrabarti, Prasun |
author_facet | Jin, Xue-Bo Wang, Zhong-Yao Kong, Jian-Lei Bai, Yu-Ting Su, Ting-Li Ma, Hui-Jun Chakrabarti, Prasun |
author_sort | Jin, Xue-Bo |
collection | PubMed |
description | The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data’s temporal information. In addition, this study used Bayesian optimization to solve the problem of the model’s inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration. |
format | Online Article Text |
id | pubmed-9955989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99559892023-02-25 Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction Jin, Xue-Bo Wang, Zhong-Yao Kong, Jian-Lei Bai, Yu-Ting Su, Ting-Li Ma, Hui-Jun Chakrabarti, Prasun Entropy (Basel) Article The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data’s temporal information. In addition, this study used Bayesian optimization to solve the problem of the model’s inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration. MDPI 2023-01-30 /pmc/articles/PMC9955989/ /pubmed/36832613 http://dx.doi.org/10.3390/e25020247 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jin, Xue-Bo Wang, Zhong-Yao Kong, Jian-Lei Bai, Yu-Ting Su, Ting-Li Ma, Hui-Jun Chakrabarti, Prasun Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title | Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title_full | Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title_fullStr | Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title_full_unstemmed | Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title_short | Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction |
title_sort | deep spatio-temporal graph network with self-optimization for air quality prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955989/ https://www.ncbi.nlm.nih.gov/pubmed/36832613 http://dx.doi.org/10.3390/e25020247 |
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