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    Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations.

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    Publication date
    2007
    Author
    Qahwaji, Rami S.R.
    Colak, Tufan
    Keyword
    Solar imaging; Space weather; Image processing; Machine learning; Sunspots; Solar cycle data; Prediction; Sunspot groups; McIntosh classification
    Peer-Reviewed
    yes
    
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    Abstract
    In this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use.
    URI
    http://hdl.handle.net/10454/4092
    Version
    Accepted Manuscript
    Citation
    Qahwaji RSR and Colak T (2007) Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations. Solar Physics. 241(1): 195-211.
    Link to publisher’s version
    http://dx.doi.org/10.1007/s11207-006-0272-5
    Type
    Article
    Collections
    Engineering and Informatics Publications

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