Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations

• Published in: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) pp. 1 - 10
• Main Authors: Pandey, Chetraj, Ji, Anli, Nandakumar, Trisha, Angryk, Rafal A., Aydin, Berkay
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.10.2023
• Subjects: Data science; Deep learning; Forecasting; Interpretability; Line-of-sight propagation; Magnetic resonance imaging; Predictive models; Shape; Solar flares; xAI
• DOI: 10.1109/DSAA60987.2023.10302639

This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast \geq\mathrm{M}-class solar flares and evaluating its efficacy on both central (within ±70°) and near-limb beyond ±70°) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings fro human-centered quantitative assessments of these explanations. Our model is trained using hourly full-disk line-of-sight magnetogram images to predict \geq{\mathrm{M}}-class solar flares within the subsequent 24-hour prediction window. Additionally, we apply the Guided Gradient-weighted Class Activation Mapping (Guided Grad-CAM) attribution method to interpret our model's predictions and evaluate the explanations. Our analysis unveils that full-disk solar flare predictions correspond with active region characteristics. The following points represent the most important findings of our study: ❨1❩ Our deep learning models achieved an average true skill statistic (TSS) of \sim 0.51 and a Heidke skill score (HSS) of \sim.38, exhibiting skill to predict solar flares where for central locations the average recall is \sim 0.75 (recall values for X- and M-class are 0.95 and 0.73 respectively) and for the near-limb flares the average recall is \sim 0.52 (recall values for X- and M- class are 0.74 and 0.50 respectively); ❨2❩ qualitative examination of the model's explanations reveals that it discerns and leverages features linked to active regions in both central and near-limb locations within full-disk magnetograms to produce respective predictions. In essence, our models grasp the shape and texture-based properties of flaring active regions, even in proximity to limb areas-a novel and essential capability with considerable significance for operational forecasting systems.