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dc.contributor.advisorQahwaji, Rami S.R.
dc.contributor.advisorIpson, Stanley S.
dc.contributor.authorAlomari, Mohammad H.*
dc.date.accessioned2010-03-03T15:53:24Z
dc.date.available2010-03-03T15:53:24Z
dc.date.issued2010-03-03T15:53:24Z
dc.identifier.urihttp://hdl.handle.net/10454/4248
dc.description.abstractCoronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations¿ datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).en
dc.language.isoenen
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.en
dc.subjectCoronal Mass Ejections (CMEs)en
dc.subjectSolar flaresen
dc.subjectSolar activitiesen
dc.subjectSpace weather forecasten
dc.subjectElectromagnetic radiationen
dc.subjectSunspot groupsen
dc.subjectSolar filamentsen
dc.subjectMachine learning techniquesen
dc.subjectMcIntosh classificationsen
dc.subjectHidden Markov Model (HMM).en
dc.titleEngineering System Design for Automated Space Weather Forecast. Designing Automatic Software Systems for the Large-Scale Analysis of Solar Data, Knowledge Extraction and the Prediction of Solar Activities Using Machine Learning Techniques.en
dc.type.qualificationleveldoctoralen
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Computing, Informatics & Mediaen
dc.typeThesiseng
dc.type.qualificationnamePhDen
dc.date.awarded2009
refterms.dateFOA2018-07-18T12:58:20Z


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