Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

• Published in: Solar physics Vol. 293; no. 2; pp. 1 - 42
• Main Authors: Florios, Kostas, Kontogiannis, Ioannis, Park, Sung-Hong, Guerra, Jordan A., Benvenuto, Federico, Bloomfield, D. Shaun, Georgoulis, Manolis K.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2018; Springer Nature B.V
• Subjects: Artificial intelligence; Astrophysics and Astroparticles; Atmospheric Sciences; Computer simulation; Entropy; Forecasting; Machine learning; Monte Carlo simulation; Objective function; Photosphere; Physics; Physics and Astronomy; Solar activity; Solar activity regions; Solar cycle; Solar flares; Solar observatories; Solar physics; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical analysis; Support vector machines; Flares, relation to magnetic field; Flares, forecasting; Active regions, magnetic fields
• ISSN: 0038-0938; 1573-093X
• DOI: 10.1007/s11207-018-1250-4

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 > M1 and > 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 ACC = 0.93 ( 0.00 ) , true skill statistic TSS = 0.74 ( 0.02 ) , and Heidke skill score HSS = 0.49 ( 0.01 ) for > M1 flare prediction with probability threshold 15% and ACC = 0.84 ( 0.00 ) , TSS = 0.60 ( 0.01 ) , and HSS = 0.59 ( 0.01 ) for > C1 flare prediction with probability threshold 35%.