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- W4385473629 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." @default.
- W4385473629 created "2023-08-02" @default.
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- W4385473629 date "2023-07-28" @default.
- W4385473629 modified "2023-10-13" @default.
- W4385473629 title "Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods" @default.
- W4385473629 doi "https://doi.org/10.1007/978-3-031-43430-3_5" @default.
- W4385473629 hasPublicationYear "2023" @default.
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