{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/exploring-deep-learning-for-full-disk-solar","title":"Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations","arxiv_id":"2308.15712","date":"2023-08-30","proceeding":null,"authors":["Chetraj Pandey","Anli Ji","Trisha Nandakumar","Rafal A. Angryk","Berkay Aydin"],"abstract":"This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\\geq$M-class solar flares and evaluating its efficacy on both central (within $\\pm$70$^\\circ$) and near-limb (beyond $\\pm$70$^\\circ$) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings from human-centered quantitative assessments of these explanations. Our model is trained using hourly full-disk line-of-sight magnetogram images to predict $\\geq$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$0.38, 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.","url_abs":"https://arxiv.org/abs/2308.15712v1","url_pdf":"https://arxiv.org/pdf/2308.15712v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"exploring-deep-learning-for-full-disk-solar","repo_url":"https://bitbucket.org/gsudmlab/fdexplainggcam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"solar-flare-prediction","task_name":"Solar Flare Prediction"}],"methods":[{"method_slug":"hoc","method_name":"HOC"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}