Nowadays, space weather has become an international issue to the world's countries
because of its catastrophic effect on space-borne and ground-based systems, and
industries, impacting our lives. One of the main solar activities that is considered as a
major driver of space weather is solar flares. Solar flares can be defined as an enormous
eruption in the sun's atmosphere. This phenomenon happens when magnetic energy stored
in twisted magnetic fields, usually near sunspots, is suddenly released. Yet, their
occurrence is not fully understood. These flares can affect the Earth by the release of
massive quantities of charged particles and electromagnetic radiation. Investigating the
associations between solar flares and sunspot groups is helpful in comprehending the
possible cause and effect relationships among solar flares and sunspot features. 01 This
thesis proposes a new approach developed by integrating advances in image processing,
machine learning, and deep learning with advances in solar physics to extract valuable
knowledge from historical solar data related to sunspot regions and flares.
This dissertation aims to achieve the following:
1) We developed a new prediction algorithm based on the Automated Solar Activity
Prediction system (ASAP) system. The proposed algorithm updates the ASAP system
by extending the training process and optimizing the learning rules to the optimize
performance better. Two neural networks are used in the proposed approach. The first
neural network is used to predict whether a specific sunspot class at a particular time
is likely to produce a significant flare or not. The second neural network is used to
predict the type of this flare, X or M-class.
2) We proposed a new system called the ASAP_Deep system built on top of the ASAP
system introduced in [6] but improves the system with an updated deep learning-based
prediction capability. In addition, we successfully apply Convolutional Neural
Network (CNN) to the sunspot group image without any pr-eprocessing or feature
extraction. Moreover, our system results are considerably better, especially for the
false alarm ratio (FAR); this reduces the losses resulting from the protection measures
applied by companies. In addition, the proposed system achieves a relatively high
score of True Skill Statistic (TSS) and Heidke Skill Score (HSS).
3) We presented a novel system that used the Deep Belief Networks (DBNs) to predict
the solar flares occurrence. The input data are SDO/HMI Intensitygram and
Magnetogram images. The model outputs are "Flare or No-Flare" of significant flare
occurrence (M and X-class flares). In addition, we created a dataset from the sunspots
groups extracted from SDO HMI Intensitygram images. We compared the results
obtained from the complete suggested system with those of three previous flare forecast models using several statistical metrics.
In our view, these developed methods and results represent an excellent initial
step toward enhancing the accuracy of flare forecasting, enhance our understanding of flare occurrence, and develop efficient flare prediction systems. The systems, implementation, results, and future work are explained in this dissertation.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19194 |
| Date | January 2021 |
| Creators | Abed, Ali K. |
| Contributors | Qahwaji, Rami S.R., Abd-Alhameed, Raed |
| 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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