On hourly prediction of PM2.5 using spatial–temporal graph convolutional network

Globally, air pollution has garnered significant attention in the past few years. Accurate air pollution like PM 2.5 hourly prediction is an essential instrument for air protection because it can be utilized in developing air pollution prevention and control techniques and provides an early warning...

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Veröffentlicht in:	Earth science informatics Jg. 18; H. 2; S. 402
Hauptverfasser:	Ren, Zhenxing, Ji, Xinxin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Schlagworte:	Air pollution Artificial neural networks Decomposition Early warning systems Earth and Environmental Science Earth Sciences Earth System Sciences Forecasting Forecasting models Information Systems Applications (incl.Internet) Meteorological data Ontology Particulate matter Permutations Pollution prevention Prediction models Search algorithms Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Beijing China China Spatial–temporal graph convolutional network Attention mechanism Hourly prediction of air pollutants Empirical mode decomposition
ISSN:	1865-0473, 1865-0481
Online-Zugang:	Volltext
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