Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting

TL;DR

This study develops and interprets a deep learning model for solar flare prediction using full-disk magnetograms, demonstrating its ability to locate and predict flares near the solar limb with explainable attention methods.

Contribution

The paper introduces a full-disk deep learning model with post hoc attention techniques for solar flare prediction, improving interpretability and operational relevance.

Findings

Model achieves TSS=0.51 and HSS=0.38 on average.

Successfully locates and predicts near-limb solar flares.

Model learns features from active regions in full-disk magnetograms.

Abstract

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.