Citation: Ahmed OW, Qahwaji RSR, Colak T, Higgins PAB, Gallagher P and Bloomfield S (2013) Solar flare prediction using advanced feature extraction, machine learning and feature selection. Solar Physics. 283(1): 157-175. Abstract: 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. ABSTRACT: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.

In this paper, we introduce two novel models for processing real-life satellite images to quantify and then visualise their magnetic structures in 3D. We believe this multidisciplinary work is a real convergence between image processing, 3D visualization and solar physics. The first model aims to calculate the value of the magnetic complexity in active regions and the solar disk. A series of experiments are carried out using this model and a relationship has been indentified between the calculated magnetic complexity values and solar flare events. The second model aims to visualise the calculated magnetic complexities in 3D colour maps in order to identify the locations of eruptive regions on the Sun. Both models demonstrate promising results and they can be potentially used in the fields of solar imaging, space weather and solar flare prediction and forecasting.

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This paper describes the application of a new method to calculate energies of solar active regions and sunspots in satellites images. Digital images from the Solar & Heliospheric Observatory (SOHO) satellite are used I n this study and energies are calculated using the Ising model, which has been modified for our application. The new algorithm has been integrated with our existing ASAP system (Automate Solar Activity Prediction) [lJ [2J and extensive testing carried out. The results obtained are promising and will help enhance the accuracy of our automated real-time solarflare prediction.

It is extremely important to design preventive measures to avoid or mitigate the influence of space weather. Severe solar activities could have a catastrophic impact on human activities in general i.e. damaging satellites, flight navigation, power distribution stations, telecommunications, etc. In this paper, a new model has been designed and implemented to calculate the energy of solar active regions and the solar disk energy. The method has been tested in a series of experiments and a relationship has been found between the calculated energies and solar flare events. A final automated realtime model will be designed later to provide flare forecasting, based on the proposed model.