Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

Abstract Efficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we...

Descrición completa

Detalles Bibliográficos

Principais autores:	Meiling Cheng, Fangxin Fang, Ionel M. Navon, Jie Zheng, Xiao Tang, Jiang Zhu, Christopher Pain
Formato:	Artigo
Idioma:	Inglês
Publicado:	American Geophysical Union (AGU) 2022-03-01
Series:	Journal of Advances in Modeling Earth Systems
Assuntos:	ozone forecasting spatio‐temporal generative adversarial network variational autoencoder deep learning
Acceso en liña:	https://doi.org/10.1029/2021MS002806
Tags:	Engadir etiqueta Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!