基于网页的太阳耀斑预测平台，支持特征驱动的机器学习分析。
PRESOL: a web-based computational setting for feature-based flare forecasting
- 构建网页平台运行特征提取与机器学习预测流程
- 可输出耀斑发生概率、关键特征排序及模型性能评估
- 适合空间天气研究者快速验证预测算法
太阳耀斑是太阳系中最剧烈的爆发现象，常引发日冕物质抛射并最终导致地球磁暴，威胁基础设施。数据驱动的耀斑预测依赖深度学习（操作性强但解释性差）或机器学习（可提供物理特征影响信息）。本文介绍一个基于网页的计算平台，支持特征驱动的机器学习方法执行，实现耀斑发生预测、特征重要性排序及预测性能评估。
原文摘要 · Abstract (English)
Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances.
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