  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.
