Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection
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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. Spatiotemporal 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 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 six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.
KeywordsActive regions, magnetic fields Flares, forecasting Photosphere Space weather Feature extraction Machine learning Feature selection
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- Colak, T., Qahwaji, R.: 2010, In: Automated Prediction of Solar Flares, LAP LAMBERT Academic Publishing, Saarbrücken, 74. Google Scholar
- Committee on the Societal and Economic Impacts of Severe Space Weather Events: 2008, Severe Space Weather Events – Understanding Societal and Economic Impacts, The National Academies Press, Washington. Google Scholar
- Hall, M.A.: 1999, Correlation-based Feature Selection for Machine Learning. PhD Thesis, The University of Waikato, Hamilton, New Zealand. Google Scholar
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: 2009, Open Source Anal. 11, 10. Google Scholar
- Liu, H., Motoda, H.: 2008, In: Computational Methods of Feature Selection, Chapman and Hall/CRC, New York, 4. Google Scholar
- Liu, H., Motoda, H., Setiono, R., Zhao, Z.: 2010, J. Mach. Learn. Res. 10, 4. Google Scholar
- NOAA Tiger Team: 2011, http://ngdc.noaa.gov/stp/satellite/anomaly/2010_sctc/docs/1-2_WDenig.pdf (retrieved 15 March 2011).
- Qahwaji, R., Colak, T.: 2006, In: Chu, H.W., Aguilar, J., Rishe, N., Azoulay, J. (eds.) 3rd International Conference on Cybernetics and Information Technologies, Internat. Inst. Informatics Systemics, Orlando, 192. Google Scholar