Automated Prediction of Solar Flares Using SDO Data. The Development of An Automated Computer System for Predicting Solar Flares Based on SDO Satellite Data Using HMI Images Analysis, Visualisation, and Deep Learning Technologies
AuthorAbed, Ali K.
SupervisorQahwaji, Rami S.R.
Abd-Alhameed, Raed A.
KeywordConvolutional neural networks
Deep belief networks
The University of Bradford theses are licenced under a Creative Commons Licence.
InstitutionUniversity of Bradford
DepartmentSchool of Computing, Informatics & Media
MetadataShow full item record
AbstractNowadays, 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  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.
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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.Qahwaji, Rami S.R.; Ipson, Stanley S.; Colak, Tufan; Ahmed, Omar W. (University of BradfordSchool of Computing, Informatics & Media, 2012-04-16)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.
A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting PatternsPark, S.H.; Leka, K.D.; Kusano, K.; Andries, J.; Barnes, G.; Bingham, S.; Bloomfield, D.S.; McCloskey, A.E.; Delouille, V.; Falconer, D.; et al. (2020-02-19)A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.
Solar flare prediction using advanced feature extraction, machine learning and feature selectionAhmed, Omar W.; Qahwaji, Rami S.R.; Colak, Tufan; Higgins, P.A.; Gallagher, P.T.; Bloomfield, D.S. (2013-03)Novel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.