Articles | Volume 27, issue 1
Nonlin. Processes Geophys., 27, 11–22, 2020
https://doi.org/10.5194/npg-27-11-2020
Nonlin. Processes Geophys., 27, 11–22, 2020
https://doi.org/10.5194/npg-27-11-2020

Research article 03 Feb 2020

Research article | 03 Feb 2020

Prediction and variation of the auroral oval boundary based on a deep learning model and space physical parameters

Yiyuan Han et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Bing Han on behalf of the Authors (29 Sep 2019)  Author's response    Manuscript
ED: Publish as is (17 Dec 2019) by Jörg Büchner
AR by Bing Han on behalf of the Authors (24 Dec 2019)  Author's response    Manuscript
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Short summary
We design a new non-linear model to construct an accurate relationship between auroral oval boundaries and 18 space physical parameters, and explore the influence of every single space physical parameter on auroral oval boundary in this paper. As a result, we found the combination of some space physical parameters can strengthen each other's influence on aurora oval boundary prediction, and this model can achieve the best performance when only partial space physical parameters are used as input.