Yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/7581 |
| Date | 03 1900 |
| Creators | Ahmed, Omar W., Qahwaji, Rami S.R., Colak, Tufan, Higgins, P.A., Gallagher, P.T., Bloomfield, D.S. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Accepted Manuscript |
| Rights | (c) 2013 Springer Verlag. Full-text reproduced in accordance with the publisher's self-archiving policy. |
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