Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network
In the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal r...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Environmental Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2022.981268/full |
| _version_ | 1811181626748567552 |
|---|---|
| author | An Zhang Sheng Chen Sheng Chen Sheng Chen Fen Zhao Xiao Dai |
| author_facet | An Zhang Sheng Chen Sheng Chen Sheng Chen Fen Zhao Xiao Dai |
| author_sort | An Zhang |
| collection | DOAJ |
| description | In the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal role in environmental governance. However, traditional data-driven approaches and deep learning methods for prediction rarely consider spatiotemporal features. Furthermore, different regions always have various implicit or hidden states, which have rarely been considered in the off-the-shelf model. To solve these problems, this study proposed a novel Spatial-Temporal Matrix Factorization Generative Adversarial Network (ST MFGAN) to capture spatiotemporal correlations and overcome the regional diversity problem at the same time. Specifically, Generative Adversarial Network (GAN) composed of graph Convolutional Network (GCN) and Long-Short-Term Memory (LSTM) network is used to generate a large amount of reliable spatiotemporal data, and matrix factorization network is used to decompose the vector output by GAN into multiple sub-networks. PM2.5 are finally combined and jointly predicted by the fusion layer. Extensive experiments show the superiority of the newly designed method. |
| first_indexed | 2024-04-11T09:19:27Z |
| format | Article |
| id | doaj.art-8a7854dc9b164d4cb6bf3b66a706e1cb |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-04-11T09:19:27Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-8a7854dc9b164d4cb6bf3b66a706e1cb2022-12-22T04:32:14ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2022-09-011010.3389/fenvs.2022.981268981268Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial networkAn Zhang0Sheng Chen1Sheng Chen2Sheng Chen3Fen Zhao4Xiao Dai5College of Public Administration, Chongqing University, Chongqing, ChinaCollege of Public Administration, Chongqing University, Chongqing, ChinaChina Institute for Development Planning, Tsinghua University, Beijing, ChinaCollaborative Innovation Center for Local Government Governance at Chongqing University, Chongqing, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Cyberspace Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, ChinaIn the context of low-carbon globalization, green development has become the common pursuit of all countries and the theme of China’s development in the new era. Fine particulate matter (PM2.5) is one of the main challenges affecting air quality, and how to accurately predict PM2.5 plays a pivotal role in environmental governance. However, traditional data-driven approaches and deep learning methods for prediction rarely consider spatiotemporal features. Furthermore, different regions always have various implicit or hidden states, which have rarely been considered in the off-the-shelf model. To solve these problems, this study proposed a novel Spatial-Temporal Matrix Factorization Generative Adversarial Network (ST MFGAN) to capture spatiotemporal correlations and overcome the regional diversity problem at the same time. Specifically, Generative Adversarial Network (GAN) composed of graph Convolutional Network (GCN) and Long-Short-Term Memory (LSTM) network is used to generate a large amount of reliable spatiotemporal data, and matrix factorization network is used to decompose the vector output by GAN into multiple sub-networks. PM2.5 are finally combined and jointly predicted by the fusion layer. Extensive experiments show the superiority of the newly designed method.https://www.frontiersin.org/articles/10.3389/fenvs.2022.981268/fullPM2.5generative adversarial networksmatrix factorizationspatiotemporal predictionenvironmental governance |
| spellingShingle | An Zhang Sheng Chen Sheng Chen Sheng Chen Fen Zhao Xiao Dai Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network Frontiers in Environmental Science PM2.5 generative adversarial networks matrix factorization spatiotemporal prediction environmental governance |
| title | Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network |
| title_full | Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network |
| title_fullStr | Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network |
| title_full_unstemmed | Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network |
| title_short | Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network |
| title_sort | good environmental governance predicting pm2 5 by using spatiotemporal matrix factorization generative adversarial network |
| topic | PM2.5 generative adversarial networks matrix factorization spatiotemporal prediction environmental governance |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2022.981268/full |
| work_keys_str_mv | AT anzhang goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork AT shengchen goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork AT shengchen goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork AT shengchen goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork AT fenzhao goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork AT xiaodai goodenvironmentalgovernancepredictingpm25byusingspatiotemporalmatrixfactorizationgenerativeadversarialnetwork |