An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction

An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction

Abstract In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still chal...

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Bibliographic Details
Main Authors: Quanchao Chen, Ruyan Ding, Xinyue Mo, Huan Li, Linxuan Xie, Jiayu Yang
Format: Article
Language:English
Published: Nature Portfolio 2024-02-01
Series:Scientific Reports
Subjects:
Air quality prediction
Spatio-temporal correlation
Graph convolutional network
Bayesian optimization
Deep learning
Online Access:https://doi.org/10.1038/s41598-024-55060-2

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