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

In this paper, a machine learning-based system that could provide automated short-term solar flares 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 Centre (NGDC) 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 if a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNN), Support Vector Machines (SVM) 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 SVM provides the best performance for predicting if a McIntosh classified sunspot group is going to flare or not but CCNN is more capable of predicting the class of the flare to erupt. A hybrid system that combines SVM and CCNN is suggested for future use.