The prediction of solar energetic particle (SEP) events or solar radiation storms is one of the most important problems in the space weather field. These events may have adverse effects on technology infrastructures and humans in space; they may also irradiate passengers and flight crews in commercial aircraft flying at polar latitudes. This paper explores the use of ≥ M2 solar flares and radio burst observations as proxies for predicting >10 MeV SEP events on Earth. These observations are manifestations of the parent event at the sun associated with the SEP event. As a consequence of processing data at the beginning of the physical process that leads to the radiation storm, the model may provide its predictions with large anticipation. The main advantage of the present approach is that the model analyzes solar data that are updated every 30 min and, as such, it may be operational; however, a disadvantage is that those SEP events associated with strong well-connected flares cannot be predicted. For the period from November 1997 to February 2014, we obtained a probability of detection of 70.2%, a false alarm ratio of 40.2%, and an average anticipation time of 9 h 52 min. In this study, the prediction model was built using decision trees, an interpretable machine learning technique. This approach leads to outputs and results comparable to those derived by the Empirical model for Solar Proton Event Real Time Alert (ESPERTA) model. The obtained decision tree shows that the best criteria to differentiate pre-SEP scenarios and non-pre-SEP scenarios are the peak and integrated flux for soft X-ray flares and the radio type III bursts.

中文翻译：

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根据太阳耀斑和无线电爆发数据预测> 10 MeV SEP事件

太阳高能粒子（SEP）事件或太阳辐射风暴的预测是空间天气领域中最重要的问题之一。这些事件可能会对技术基础设施和太空人类产生不利影响；它们也可能会照射极地纬度的商用飞机上的乘客和机组人员。本文探讨了使用≥M2的太阳耀斑和无线电爆炸观测作为预测地球上> 10 MeV SEP事件的代理。这些观察结果是与SEP事件相关的太阳下父事件的表现。作为在导致辐射风暴的物理过程开始时处理数据的结果，该模型可能会对其预测提供很大的期望。本方法的主要优点是该模型可以分析每30分钟更新一次的太阳能数据，因此可以运行。但是，缺点是无法预测与强连通火炬相关的那些SEP事件。在1997年11月至2014年2月的这段时间内，我们获得了70.2％的检测概率，40.2％的误报率以及9 h 52 min的平均预期时间。在这项研究中，使用决策树（一种可解释的机器学习技术）构建了预测模型。这种方法的输出和结果可与太阳质子事件实时警报（ESPERTA）模型的经验模型得出的结果和结果相媲美。