A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM(2.5) Concentration Prediction

Accurate and fine-grained prediction of PM(2.5) concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spat...

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Detalles Bibliográficos
Autores principales: Lin, Shaofu, Zhao, Junjie, Li, Jianqiang, Liu, Xiliang, Zhang, Yumin, Wang, Shaohua, Mei, Qiang, Chen, Zhuodong, Gao, Yuyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407057/
https://www.ncbi.nlm.nih.gov/pubmed/36010788
http://dx.doi.org/10.3390/e24081125

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407057/
https://www.ncbi.nlm.nih.gov/pubmed/36010788
http://dx.doi.org/10.3390/e24081125

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