Articles | Volume 27, issue 1
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, Bing Han, Zejun Hu, Xinbo Gao, Lixia Zhang, Huigen Yang, and Bin Li


Interactive discussion

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

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
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.