Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

Basit Öğe Kaydı
dc.contributor.authorAlobaid, Khalid A.
dc.contributor.authorWang, Jason T. L.
dc.contributor.authorWang, Haimin
dc.contributor.authorJing, Ju
dc.contributor.authorAbduallah, Yasser
dc.contributor.authorWang, Zhenduo
dc.contributor.authorFarooki, Hameedullah
dc.date.accessioned2025-01-27T20:22:53Z
dc.date.available2025-01-27T20:22:53Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness are crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth's magnetosphere during which the minimum Dst index value is less than -50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT, and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew's correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.
dc.description.sponsorshipKing Saud University, Saudi Arabia; NSF [AGS-2149748, AGS-2228996, OAC-2320147, AGS-2300341, AST-2108235, AGS-2114201, AGS-2309939]; NASA [80NSSC24K0843, 80NSSC24M0174]
dc.description.sponsorshipK.A. is supported by King Saud University, Saudi Arabia. J.W. and H.W. acknowledge supportfrom NSF grants AGS-2149748, AGS-2228996, OAC-2320147, and NASA grants 80NSSC24K0843 and 80NSSC24M0174. J.J. acknowledges support from NSF grants AGS-2149748 and AGS-2300341. V.Y. acknowledges support from NSF grants AST-2108235, AGS-2114201, AGS-2300341, and AGS-2309939.
dc.identifier.doi10.1007/s11207-024-02385-w
dc.identifier.issn0038-0938
dc.identifier.issn1573-093X
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85209747548
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11207-024-02385-w
dc.identifier.urihttps://hdl.handle.net/20.500.12428/22061
dc.identifier.volume299
dc.identifier.wosWOS:001359475100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSolar Physics
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectCoronal mass ejections
dc.subjectSolar-terrestrial relations
dc.subjectHeliosphere
dc.titlePrediction of Geoeffective CMEs Using SOHO Images and Deep Learning
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu