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dc.contributor.authorAl-Omari, M.*
dc.contributor.authorQahwaji, Rami S.R.*
dc.contributor.authorColak, Tufan*
dc.contributor.authorIpson, Stanley S.*
dc.date.accessioned2016-01-29T10:53:19Z
dc.date.available2016-01-29T10:53:19Z
dc.date.issued2010-04
dc.identifier.citationAl-Omari M, Qahwaji RSR, Colak T and Ipson SS (2010) Machine learning-based investigation of the association between CMEs and filaments. Solar Physics. 262(2): 511-539.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7731
dc.descriptionYesen_US
dc.description.abstractIn this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The Support Vector Machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the NGDC filament catalogue and the SOHO/LASCO CME catalogue are processed to associate filaments with CMEs. In the previous studies which classified CMEs into gradual and impulsive CMEs, the associations were refined based on CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data- mining system has been created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that could have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance.en_US
dc.language.isoenen_US
dc.rights(c) 2010 Springer Netherlands. Full-text reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectCoronal mass ejections; Filaments; Machine learning; Prominences; Space weatheren_US
dc.titleMachine learning-based investigation of the association between CMEs and filamentsen_US
dc.status.refereedYesen_US
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
dc.identifier.doihttps://doi.org/10.1007/s11207-010-9516-5
refterms.dateFOA2018-07-25T13:21:37Z


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