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Short-term solar eruptive activity prediction models based on machine learning approaches: A review.

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Title: Short-term solar eruptive activity prediction models based on machine learning approaches: A review.
Authors: Huang, Xin1,2 (AUTHOR) huangxin@nbu.edu.cn, Zhao, Zhongrui2,3,4 (AUTHOR), Zhong, Yufeng2,3 (AUTHOR), Xu, Long1,2 (AUTHOR), Korsós, Marianna B.5,6,7 (AUTHOR), Erdélyi, R.6,7,8 (AUTHOR)
Source: SCIENCE CHINA Earth Sciences. Dec2024, Vol. 67 Issue 12, p3727-3764. 38p.
Subjects: Coronal mass ejections, Machine learning, Solar flares, Space environment, Solar activity
Abstract: Solar eruptive activities, mainly including solar flares, coronal mass ejections (CME), and solar proton events (SPE), have an important impact on space weather and our technosphere. The short-term solar eruptive activity prediction is an active field of research in the space weather prediction. Numerical, statistical, and machine learning methods are proposed to build prediction models of the solar eruptive activities. With the development of space-based and ground-based facilities, a large amount of observational data of the Sun is accumulated, and data-driven prediction models of solar eruptive activities have made a significant progress. In this review, we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction, summarize the prediction modeling process, overview the progress made in the field of solar eruptive activity prediction model, and look forward to the possible directions in the future. [ABSTRACT FROM AUTHOR]
Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Coronal+mass+ejections%22">Coronal mass ejections</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+flares%22">Solar flares</searchLink><br /><searchLink fieldCode="DE" term="%22Space+environment%22">Space environment</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+activity%22">Solar activity</searchLink>
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  Data: Solar eruptive activities, mainly including solar flares, coronal mass ejections (CME), and solar proton events (SPE), have an important impact on space weather and our technosphere. The short-term solar eruptive activity prediction is an active field of research in the space weather prediction. Numerical, statistical, and machine learning methods are proposed to build prediction models of the solar eruptive activities. With the development of space-based and ground-based facilities, a large amount of observational data of the Sun is accumulated, and data-driven prediction models of solar eruptive activities have made a significant progress. In this review, we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction, summarize the prediction modeling process, overview the progress made in the field of solar eruptive activity prediction model, and look forward to the possible directions in the future. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Solar flares
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              Text: Dec2024
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