Predicting Solar Flares using CNN and LSTM on Two Solar Cycles of Active
Region Data
Abstract
We consider the flare prediction problem that distinguishes
flare-imminent active regions that produce an M- or X-class flare in the
future 24 hours, from quiet active regions that do not produce any flare
within $\pm 24$ hours. Using line-of-sight magnetograms
and parameters of active regions in two data products covering Solar
Cycle 23 and 24, we train and evaluate two deep learning
algorithms—CNN and LSTM—and their stacking ensembles. The decisions
of CNN are explained using visual attribution methods. We have the
following three main findings. (1) LSTM trained on data from two solar
cycles achieves significantly higher True Skill Scores (TSS) than that
trained on data from a single solar cycle with a confidence level of at
least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that
combines predictions from LSTM and CNN using the TSS criterion achieves
significantly higher TSS than the “select-best” strategy with a
confidence level of at least 0.95. (3) A visual attribution method
called Integrated Gradients is able to attribute the CNN’s predictions
of flares to the emerging magnetic flux in the active region. It also
reveals a limitation of CNN as a flare prediction method using
line-of-sight magnetograms: it treats the polarity artifact of
line-of-sight magnetograms as positive evidence of flares.