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Forecasting Solar Flares using magnetogram-based predictors and Machine Learning

Kostas Florios(cflorios***at***aueb.gr)
Ioannis Kontogiannis(jkonto***at***noa.gr)
Sung-Hong Park(sunpark***at***tcd.ie)
Jordan A. Guerra(guerraaj***at***tcd.ie)
Federico Benvenuto(benvenuto***at***dima.unige.it)
D. Shaun Bloomfield(shaun.bloomfield***at***northumbria.ac.uk)
Manolis K. Georgoulis(manolis.georgoulis***at***academyofathens.gr)

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 data, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of class >M1 and >C1, with a 24h 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 a >M1 class flares prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for a >C1 class flares prediction with probability threshold 35%.

Keywords: Flares, Forecasting; Flares, Relation to Magnetic Field; Active Regions, Magnetic Fields

Category 1: Applications -- Science and Engineering (Basic Sciences Applications )

Category 2: Applications -- Science and Engineering (Data-Mining )

Category 3: Applications -- Science and Engineering (Statistics )

Citation: Technical Report, Research Center for Astronomy and Applied Mathematics, Academy of Athens, Greece, December 2017.

Download: [PDF]

Entry Submitted: 12/08/2017
Entry Accepted: 12/08/2017
Entry Last Modified: 12/08/2017

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