Explaining Full-disk Deep Learning Model for Solar Flare Prediction
  using Attribution Methods
Chetraj Pandey Rafal A. Angryk Berkay Aydin
This paper contributes to the growing body of research on deep learning
methods for solar flare prediction, primarily focusing on highly overlooked
near-limb flares and utilizing the attribution methods to provide a post hoc
qualitative explanation of the model's predictions. We present a solar flare
prediction model, which is trained using hourly full-disk line-of-sight
magnetogram images and employs a binary prediction mode to forecast
$\geq$M-class flares that may occur within the following 24-hour period. To
address the class imbalance, we employ a fusion of data augmentation and class
weighting techniques; and evaluate the overall performance of our model using
the true skill statistic (TSS) and Heidke skill score (HSS). Moreover, we
applied three attribution methods, namely Guided Gradient-weighted Class
Activation Mapping, Integrated Gradients, and Deep Shapley Additive
Explanations, to interpret and cross-validate our model's predictions with the
explanations. Our analysis revealed that full-disk prediction of solar flares
aligns with characteristics related to active regions (ARs). In particular, the
key findings of this study are: (1) our deep learning models achieved an
average TSS=0.51 and HSS=0.35, and the results further demonstrate a competent
capability to predict near-limb solar flares and (2) the qualitative analysis
of the model explanation indicates that our model identifies and uses features
associated with ARs in central and near-limb locations from full-disk
magnetograms to make corresponding predictions. In other words, our models
learn the shape and texture-based characteristics of flaring ARs even at
near-limb areas, which is a novel and critical capability with significant
implications for operational forecasting.