Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods

• URL: http://arxiv.org/abs/2307.15878v1
• Date: Sat, 29 Jul 2023 03:18:56 GMT
• Title: Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods
• Authors: Chetraj Pandey, Rafal A. Angryk and Berkay Aydin
• Abstract summary: We present a solar flare prediction model, which is trained using hourly full-disk line-of-sight magnetogram images. We evaluate the overall performance of our model using the true skill statistic (TSS) and Heidke skill score (HSS) Our analysis revealed that full-disk prediction of solar flares aligns with characteristics related to active regions (ARs)
• Score: 0.6882042556551611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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