让太阳风暴预测模型可解释，看清它怎么判断爆发与喷发关联。
Explainable AI in Deep Learning-Based Prediction of Solar Storms
- 用LSTM+注意力机制处理太阳活动区时间序列数据
- 通过后验分析揭示影响预测的关键因子
- 首个实现可解释性的太阳风暴预测模型，适合空间天气研究者
深度学习模型常被视为黑箱，其内部运作对用户不透明，难以理解预测依据。本文提出一种使基于深度学习的太阳风暴预测模型可解释的方法，涵盖太阳耀斑和日冕物质抛射（CMEs）。该模型基于长短期记忆网络（LSTM）并引入注意力机制，旨在预测太阳表面某活跃区（AR）在24小时内产生耀斑的同时是否伴随CME。核心方法是将活跃区的数据样本建模为时间序列，利用LSTM捕捉其动态变化。为确保预测可追溯、可靠，采用事后模型无关的可解释性技术，揭示输入序列对应预测结果的关键贡献因素，并提供跨多个序列的模型行为洞察。据我们所知，这是首次将可解释性引入基于LSTM的太阳风暴预测模型。
原文摘要 · Abstract (English)
A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a deep learning-based solar storm prediction model interpretable, where solar storms include solar flares and coronal mass ejections (CMEs). This deep learning model, built based on a long short-term memory (LSTM) network with an attention mechanism, aims to predict whether an active region (AR) on the Sun's surface that produces a flare within 24 hours will also produce a CME associated with the flare. The crux of our approach is to model data samples in an AR as time series and use the LSTM network to capture the temporal dynamics of the data samples. To make the model's predictions accountable and reliable, we leverage post hoc model-agnostic techniques, which help elucidate the factors contributing to the predicted output for an input sequence and provide insights into the model's behavior across multiple sequences within an AR. To our knowledge, this is the first time that interpretability has been added to an LSTM-based solar storm prediction model.
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