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dc.contributor.authorColak, Tufan
dc.contributor.authorQahwaji, Rami S.R.
dc.date.accessioned2009-12-15T16:21:08Z
dc.date.available2009-12-15T16:21:08Z
dc.date.issued04/06/2009
dc.identifier.citationColak T, and Qahwaji RSR (2009) Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares. Space Weather. 7(6).en
dc.identifier.urihttp://hdl.handle.net/10454/4106
dc.descriptionyesen
dc.description.abstractThe importance of real-time processing of solar data especially for space weather applications is increasing continuously. In this paper, we present an automated hybrid computer platform for the short-term prediction of significant solar flares using SOHO/Michelson Doppler Imager images. This platform is called the Automated Solar Activity Prediction tool (ASAP). This system integrates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyze years of sunspot and flare data to create associations that can be represented using computer-based learning rules. An imaging-based real-time system that provides automated detection, grouping, and then classification of recent sunspots based on the McIntosh classification is also created and integrated within this system. The properties of the sunspot regions are extracted automatically by the imaging system and processed using the machine learning rules to generate the real-time predictions. Several performance measurement criteria are used and the results are provided in this paper. Also, quadratic score is used to compare the prediction results of ASAP with NOAA Space Weather Prediction Center (SWPC) between 1999 and 2002, and it is shown that ASAP generates more accurate predictions compared to SWPC.en
dc.description.sponsorshipEPSRCen
dc.language.isoenen
dc.publisherAGUen
dc.relation.isreferencedbyhttp://www.agu.org/pubs/crossref/2009/2008SW000401.shtmlen
dc.rights© 2009 American Geophysical Union. Reproduced in accordance with the publisher's self-archiving policy.en
dc.subjectSolar Imagingen
dc.subjectSpace Weatheren
dc.subjectImage Processingen
dc.subjectMachine Learningen
dc.subjectSolar flaresen
dc.subjectPredictionen
dc.subjectSunspotsen
dc.titleAutomated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flaresen
dc.status.refereedyesen
dc.typeArticleen
dc.identifier.JournalTitleSpace Weather, 7, S06001, doi:10.1029/2008SW000401.en
dc.type.versionAccepted Manuscripten
refterms.dateFOA2018-07-18T19:39:38Z


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