Towards Interpretable Solar Flare Prediction with Attention-based Deep
  Neural Networks
Chetraj Pandey Anli Ji Rafal A. Angryk Berkay Aydin
Solar flare prediction is a central problem in space weather forecasting and
recent developments in machine learning and deep learning accelerated the
adoption of complex models for data-driven solar flare forecasting. In this
work, we developed an attention-based deep learning model as an improvement
over the standard convolutional neural network (CNN) pipeline to perform
full-disk binary flare predictions for the occurrence of $\geq$M1.0-class
flares within the next 24 hours. For this task, we collected compressed images
created from full-disk line-of-sight (LoS) magnetograms. We used data-augmented
oversampling to address the class imbalance issue and used true skill statistic
(TSS) and Heidke skill score (HSS) as the evaluation metrics. Furthermore, we
interpreted our model by overlaying attention maps on input magnetograms and
visualized the important regions focused on by the model that led to the
eventual decision. The significant findings of this study are: (i) We
successfully implemented an attention-based full-disk flare predictor ready for
operational forecasting where the candidate model achieves an average
TSS=0.54$\pm$0.03 and HSS=0.37$\pm$0.07. (ii) we demonstrated that our
full-disk model can learn conspicuous features corresponding to active regions
from full-disk magnetogram images, and (iii) our experimental evaluation
suggests that our model can predict near-limb flares with adept skill and the
predictions are based on relevant active regions (ARs) or AR characteristics
from full-disk magnetograms.