Liu, J., Ye, Y., Shen, C. et al. (2 more authors) (2018) A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA. Astrophysical Journal, 855 (2). 109. ISSN 0004-637X
Abstract
Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018. The American Astronomical Society. Reproduced in accordance with the publisher's self-archiving policy. |
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/L006316/1 SCIENCE AND TECHNOLOGY FACILITIES COUNCIL ST/M000826/1 THE ROYAL SOCIETY IE141493 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Apr 2018 09:19 |
Last Modified: | 05 Apr 2018 09:19 |
Published Version: | https://doi.org/10.3847/1538-4357/aaae69 |
Status: | Published |
Publisher: | American Astronomical Society |
Refereed: | Yes |
Identification Number: | 10.3847/1538-4357/aaae69 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129183 |