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Title:
Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection
Authors:
Ahmed, Omar W.; Qahwaji, Rami; Colak, Tufan; Higgins, Paul A.; Gallagher, Peter T.; Bloomfield, D. Shaun
Affiliation:
AA(School of Computing Informatics and Media, University of Bradford), AB(School of Computing Informatics and Media, University of Bradford), AC(School of Computing Informatics and Media, University of Bradford), AD(Astrophysics Research Group, School of Physics, Trinity College Dublin), AE(Astrophysics Research Group, School of Physics, Trinity College Dublin), AF(Astrophysics Research Group, School of Physics, Trinity College Dublin)
Publication:
Solar Physics, Volume 283, Issue 1, pp.157-175 (SoPh Homepage)
Publication Date:
03/2013
Origin:
SPRINGER
Keywords:
Active regions, magnetic fields, Flares, forecasting, Photosphere, Space weather, Feature extraction, Machine learning, Feature selection
Abstract Copyright:
(c) 2013: Springer Science+Business Media B.V.
DOI:
10.1007/s11207-011-9896-1
Bibliographic Code:
2013SoPh..283..157A

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. Spatiotemporal 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 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 six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.
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