SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

By Monica G. Bobra, S. Couvidat. 2014

325 citations recorded by the source corpus.

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a database of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.

Subfields: Astronomy and Astrophysics, Geophysics

View Paper: https://www.semanticscholar.org/paper/bd19e0d2fb724d2efbad5281a5dd975b9c451df7

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Citations in the corpus

Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Products and the Random Forest Algorithm (2017, methodology): “By looking at the TSS score, it is apparent that the performance of our method coupling SDO/HMI magnetic parameters with RF (TSS ≈ 0.53) is very similar to Bloomfield et al. (2012) and Ahmed et al. (2013), while being ∼28% lower than Bobra & Couvidat (2015) and Nishizuka et al. (2017).”
WHY IS THE GREAT SOLAR ACTIVE REGION 12192 FLARE-RICH BUT CME-POOR? (2015, background): “A comprehensive statistical study is now possible and has been performed for flare likelihood (Bobra & Couvidat 2015).”
A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast (2016, methodology): “Previously, various machine learning techniques have been applied to flare prediction algorithms: support vector machines (SVM) [Li et al., 2007; Bobra and Couvidat , 2014; Muranushi et al., 2015], ordinal logistic regression [Song et al., 2009; Yuan et al., 2010], neural networks [Colak and…”
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting (2019, methodology): “In Bobra and Ilonidis (2016), the authors have developed a classifier based on Support Vector Machine to study features that can distinguish between the two cases and eventually to forecast whether an active region can produce an M or X class flare.”