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Enhanced flare prediction by advanced feature extraction from solar images : developing automated imaging and machine learning techniques for processing solar images and extracting features from active regions to enable the efficient prediction of solar flares
Ahmed, Omar W.
Ahmed, Omar W.
Publication Date
2012-04-16
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The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
School of Computing, Informatics & Media
Awarded
2011
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Abstract
Space weather has become an international issue due to the catastrophic impact
it can have on modern societies. Solar flares are one of the major solar activities that
drive space weather and yet their occurrence is not fully understood. Research is
required to yield a better understanding of flare occurrence and enable the development
of an accurate flare prediction system, which can warn industries most at risk to take
preventative measures to mitigate or avoid the effects of space weather. This thesis
introduces novel technologies developed by combining advances in statistical physics,
image processing, machine learning, and feature selection algorithms, with advances in
solar physics in order to extract valuable knowledge from historical solar data, related to
active regions and flares. The aim of this thesis is to achieve the followings: i) The
design of a new measurement, inspired by the physical Ising model, to estimate the
magnetic complexity in active regions using solar images and an investigation of this
measurement in relation to flare occurrence. The proposed name of the measurement is
the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction
capability of active region properties generated by the new active region detection
system SMART (Solar Monitor Active Region Tracking) to enable the design of a new
flare prediction system. iii) Determination of the active region properties that are most
related to flare occurrence in order to enhance understanding of the underlying physics
behind flare occurrence. The achieved results can be summarised as follows: i) The new
active region measurement (IMC) appears to be related to flare occurrence and it has a
potential use in predicting flare occurrence and location. ii) Combining machine
learning with SMART¿s active region properties has the potential to provide more
accurate flare predictions than the current flare prediction systems i.e. ASAP
(Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties
seems to be the most significant properties related to flare occurrence and they can
achieve similar degree of flare prediction accuracy as the full 21 SMART active region
properties. The developed technologies and the findings achieved in this thesis will
work as a corner stone to enhance the accuracy of flare prediction; develop efficient
flare prediction systems; and enhance our understanding of flare occurrence. The
algorithms, implementation, results, and future work are explained in this thesis.
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Type
Thesis
Qualification name
PhD