Open Access

Table 1.

Description of twelve flare forecasting studies based on machine learning.

Paper Data Multiple test AR data split Validation Method Score – C1+ Score – M1+
realizations
Bobra & Couvidat (2015) Point in time Yes Yes Yes SVM 0.74
features (SHARP)

Liu et al. (2017) Point in time Yes Yes Yes RF 0.76
features (SHARP)

Nishizuka et al. (2018) Point in time No No No MLP 0.63 0.80
features (ad hoc computed)

Florios et al. (2018) Point in time Yes Yes Yes RF 0.60 0.74
features (FLARECAST)

Jonas et al. (2018) Time series Yes No Yes RF 0.74−0.81
features

Campi et al. (2019) Point in time Yes No Yes Hybrid lasso 0.54 0.67
features (FLARECAST)

Liu et al. (2019) Time series Yes No Yes LSTM 0.61 0.79
features (SHARP)

Wang et al. (2020) Time series ... No yes LSTM 0.55 0.68
features (SHARP)

Park et al. (2018) HMI and No No Yes CNN 0.63 ...
MDI magnetograms

Huang et al. (2018) HMI and Yes Yes CNN 0.49 0.66
MDI magnetograms

Li et al. (2020) HMI Yes No No CNN 0.68 0.75
magnetograms

Yi et al. (2021) HMI No No Yes CNN 0.65
magnetograms

Notes. For each study, the table reports: the main author (column “paper”); the kind of data used (column “data”); whether a confidence strip has been computed for the skill score (column “multiple test realizations”); whether data belonging to the same AR are split between the training and test sets (column “AR data split”); whether a validation set has been used to optimize the machine learning algorithm (column “validation”); which method has been used (column “method”); and the score values for the prediction of C1+ and M1+ flares (columns “score – C1+” and “score – M1+”).

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