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dc.contributor.authorAhmed, Omar W.
dc.contributor.authorQahwaji, Rami S.R.
dc.contributor.authorColak, Tufan
dc.contributor.authorHiggins, P.A.
dc.contributor.authorGallagher, P.T.
dc.contributor.authorBloomfield, D.S.
dc.date.accessioned2015-12-22T10:52:37Z
dc.date.available2015-12-22T10:52:37Z
dc.date.issued2013-03
dc.identifier.citationAhmed OW, Qahwaji RSR, Colak T, Higgins PA, Gallagher PT and Bloomfield DS (2013) Solar flare prediction using advanced feature extraction, machine learning and feature selection. Solar Physics. 283(1): 157-175.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7581
dc.descriptionYesen_US
dc.description.abstractNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1007/s11207-011-9896-1en_US
dc.rights(c) 2013 Springer Verlag. Full-text reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectActive regions; Magnetic fields; Flares; Forecasting; Photosphere; Space weather; feature extraction; Machine learning; Feature selectionen_US
dc.titleSolar flare prediction using advanced feature extraction, machine learning and feature selectionen_US
dc.status.refereedYesen_US
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-25T14:47:10Z


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