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    Solar flare prediction using advanced feature extraction, machine learning and feature selection

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    Publication date
    2013-03
    Author
    Ahmed, Omar W.
    Qahwaji, Rami S.R.
    Colak, Tufan
    Higgins, P.A.
    Gallagher, P.T.
    Bloomfield, D.S.
    Keyword
    Active regions; Magnetic fields; Flares; Forecasting; Photosphere; Space weather; feature extraction; Machine learning; Feature selection
    Rights
    (c) 2013 Springer Verlag. Full-text reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    
    Metadata
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    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.
    URI
    http://hdl.handle.net/10454/7581
    Version
    Accepted Manuscript
    Citation
    Ahmed OW, Qahwaji RSR, Colak T, Higgins PA, Gallagher PT and Bloomfield DS (2013) Solar flare prediction using advanced feature extraction, machine learning and feature selection. Solar Physics. 283(1): 157-175.
    Link to publisher’s version
    http://dx.doi.org/10.1007/s11207-011-9896-1
    Type
    Article
    Collections
    Engineering and Informatics Publications

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