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

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Title: Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks.
Authors: Cheng, Meiling1 (AUTHOR), Fang, Fangxin1 (AUTHOR) f.fang@imperial.ac.uk, Navon, Ionel M.2 (AUTHOR), Zheng, Jie3 (AUTHOR), Tang, Xiao4 (AUTHOR), Zhu, Jiang4 (AUTHOR), Pain, Christopher1 (AUTHOR)
Source: Journal of Advances in Modeling Earth Systems. Mar2022, Vol. 14 Issue 3, p1-26. 26p.
Database: Academic Search Ultimate
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An: 155977519
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  Data: Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advances+in+Modeling+Earth+Systems%22">Journal of Advances in Modeling Earth Systems</searchLink>. Mar2022, Vol. 14 Issue 3, p1-26. 26p.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1029/2021MS002806
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        Text: English
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        PageCount: 26
        StartPage: 1
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      – TitleFull: Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks.
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            NameFull: Cheng, Meiling
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            NameFull: Fang, Fangxin
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            NameFull: Zheng, Jie
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            NameFull: Tang, Xiao
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            NameFull: Zhu, Jiang
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            – D: 01
              M: 03
              Text: Mar2022
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              Y: 2022
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            – TitleFull: Journal of Advances in Modeling Earth Systems
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