Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations |
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Authors: | R Qahwaji T Colak |
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Institution: | (1) Department of Electronic Imaging and Media Communications, University of Bradford, Richmond Road, Bradford, BD7 1DP, England, UK |
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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. |
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