Predicting CME arrival time through data integration and ensemble learning

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dc.authoridAlobaid, Khalid A./0009-0007-4731-2772
dc.authoridCavus, Huseyin/0000-0003-4224-7039
dc.authoridJIANG, HAODI/0000-0001-6460-408X
dc.authoridYurchyshyn, Vasyl/0000-0001-9982-2175
dc.contributor.authorAlobaid, Khalid A.
dc.contributor.authorAbduallah, Yasser
dc.contributor.authorWang, Jason T. L.
dc.contributor.authorWang, Haimin
dc.contributor.authorJiang, Haodi
dc.contributor.authorXu, Yan
dc.contributor.authorYurchyshyn, Vasyl
dc.date.accessioned2025-01-27T20:22:53Z
dc.date.available2025-01-27T20:22:53Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.
dc.description.sponsorshipU.S. NSF; [AGS-1927578]; [AGS-1954737]; [AGS-2149748]
dc.description.sponsorshipThis work was supported by U.S. NSF grants AGS-1927578, AGS-1954737, and AGS-2149748.
dc.identifier.doi10.3389/fspas.2022.1013345
dc.identifier.issn2296-987X
dc.identifier.scopus2-s2.0-85140592301
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3389/fspas.2022.1013345
dc.identifier.urihttps://hdl.handle.net/20.500.12428/22060
dc.identifier.volume9
dc.identifier.wosWOS:000875496500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Astronomy and Space Sciences
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectheliophysics
dc.subjectspace weather
dc.subjectcoronal mass ejections
dc.subjectinterplanetary shocks
dc.subjectmachine learning
dc.titlePredicting CME arrival time through data integration and ensemble learning
dc.typeArticle

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