Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning |
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| Authors: | Kostas Florios " target="_blank">Ioannis Kontogiannis " target="_blank">Sung-Hong Park " target="_blank">Jordan A Guerra " target="_blank">Federico Benvenuto " target="_blank">D Shaun Bloomfield " target="_blank">Manolis K Georgoulis |
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| Institution: | 1.Research Center for Astronomy and Applied Mathematics,Academy of Athens,Athens,Greece;2.Department of Statistics,Athens University of Economics and Business,Athens,Greece;3.School of Physics,Trinity College Dublin,Dublin,Ireland;4.Dipartimento di Matematica,Università di Genova,Genoa,Italy;5.Northumbria University,Newcastle upon Tyne,UK |
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| Abstract: | We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012?–?2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude \({>}\,\mbox{M1}\) and \({>}\,\mbox{C1}\) within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy \(\mathrm{ACC}=0.93(0.00)\), true skill statistic \(\mathrm{TSS}=0.74(0.02)\), and Heidke skill score \(\mathrm{HSS}=0.49(0.01)\) for \({>}\,\mbox{M1}\) flare prediction with probability threshold 15% and \(\mathrm{ACC}=0.84(0.00)\), \(\mathrm{TSS}=0.60(0.01)\), and \(\mathrm{HSS}=0.59(0.01)\) for \({>}\,\mbox{C1}\) flare prediction with probability threshold 35%. |
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