Wang, Y., Liu, J. orcid.org/0000-0003-2569-1840, Jiang, Y. et al. (1 more author) (2019) CME arrival time prediction using convolutional neural network. The Astrophysical Journal, 881 (1). 15. ISSN 0004-637X
Abstract
Fast and accurate prediction of the arrival time of coronal mass ejections (CMEs) at Earth is vital to minimize hazards caused by CMEs. In this paper, we use a deep-learning framework, i.e., a convolutional neural network (CNN) regression model, to analyze transit times from the Sun to Earth of 223 geoeffective CME events observed in the past 30 yr. 90% of them were used to build the prediction model, and the rest 10% have been used for test purpose. Unlike previous studies on this topic, our proposed CNN regression model does not require manually selected features for model training, it does not need time spent on feature collection, and it can deliver predictions without deeper expert knowledge. The only input to our CNN regression model is the instances of the white-light observations of CMEs. The mean absolute error of the constructed CNN regression model is about 12.4 hr, which is comparable to the average performance of the previous studies on this subject. As more CME data become available, we expect the CNN regression model will reveal better results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019. The American Astronomical Society. This is an author-produced version of a paper subsequently published in The Astrophysical Journal. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | solar–terrestrial relations; Sun: coronal mass ejections (CMEs); techniques: image processing; Convolution Neural Network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number Science and Technology Facilities Council ST/M000826/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Aug 2019 09:12 |
Last Modified: | 12 Aug 2019 11:05 |
Status: | Published |
Publisher: | American Astronomical Society |
Refereed: | Yes |
Identification Number: | 10.3847/1538-4357/ab2b3e |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149545 |