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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. January 2011 (has links)
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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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 flaresAhmed, Omar Wahab January 2011 (has links)
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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