Accurate and reliable predictions of solar flares are essential due to their potentially significant impact on Earth and space-based infrastructure. Although deep learning models have shown notable predictive capabilities in this domain, current evaluations often focus on accuracy while neglecting interpretability and reliability--factors that are especially critical in operational settings. To address this gap, we propose a novel proximity-based framework for analyzing post hoc explanations to assess the interpretability of deep learning models for solar flare prediction. Our study compares two models trained on full-disk line-of-sight (LoS) magnetogram images to predict $\geq$M-class solar flares within a 24-hour window. We employ the Guided Gradient-weighted Class Activation Mapping (Guided Grad-CAM) method to generate attribution maps from these models, which we then analyze to gain insights into their decision-making processes. To support the evaluation of explanations in operational systems, we introduce a proximity-based metric that quantitatively assesses the accuracy and relevance of local explanations when regions of interest are known. Our findings indicate that the models' predictions align with active region characteristics to varying degrees, offering valuable insights into their behavior. This framework enhances the evaluation of model interpretability in solar flare forecasting and supports the development of more transparent and reliable operational systems.

本研究解决了太阳耀斑预测模型在可解释性和可靠性方面的评估不足的问题。我们提出了一种基于邻近度的框架，利用Guided Grad-CAM方法分析模型决策过程，并引入定量评估指标，以提高模型在实际应用中的透明度和可靠性。研究结果显示，模型预测与活跃区特征的对齐程度不一，为太阳耀斑预测的可解释性评估提供了重要见解。

基于深度学习的可解释太阳耀斑预测模型的大规模评估与归因近似分析