Coronal 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).
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/4248 |
| Date | January 2009 |
| Creators | Alomari, Mohammad H. |
| Contributors | Qahwaji, Rami S.R., Ipson, Stanley S. |
| Publisher | University of Bradford, School of Computing, Informatics & Media |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| 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>. |
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