Explainable Deep Learning-based Solar Flare Prediction with post hoc
  Attention for Operational Forecasting
Chetraj Pandey Rafal A. Angryk Manolis K. Georgoulis Berkay Aydin
This paper presents a post hoc analysis of a deep learning-based full-disk
solar flare prediction model. We used hourly full-disk line-of-sight
magnetogram images and selected binary prediction mode to predict the
occurrence of $\geq$M1.0-class flares within 24 hours. We leveraged custom data
augmentation and sample weighting to counter the inherent class-imbalance
problem and used true skill statistic and Heidke skill score as evaluation
metrics. Recent advancements in gradient-based attention methods allow us to
interpret models by sending gradient signals to assign the burden of the
decision on the input features. We interpret our model using three post hoc
attention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii)
Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Our
analysis shows that full-disk predictions of solar flares align with
characteristics related to the active regions. The key findings of this study
are: (1) We demonstrate that our full disk model can tangibly locate and
predict near-limb solar flares, which is a critical feature for operational
flare forecasting, (2) Our candidate model achieves an average
TSS=0.51$\pm$0.05 and HSS=0.38$\pm$0.08, and (3) Our evaluation suggests that
these models can learn conspicuous features corresponding to active regions
from full-disk magnetograms.