Spatiotemporal graph neural networks for predicting mid-to-long-term PM2.5 concentrations

Author:

Kim Do-YeonORCID,Jin Dae-YongORCID,Suk Heung-Il

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment,Building and Construction

Reference37 articles.

1. Hypergraph convolution and hypergraph attention;Bai;Pattern Recogn.,2021

2. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System;Byun,2006

3. Selection of key features for PM2.5 prediction using a wavelet model and RBF-LSTM;Chen;Appl. Intell.,2021

4. Influence of meteorological conditions on PM2.5 concentrations across China: a review of methodology and mechanism;Chen;Environ. Int.,2020

5. Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks;Chiang,2019

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