The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of a SPE, i.e., they are based on the classification approach. In this work we present a simple and efficient machine learning regression algorithm which is able to forecast the energetic proton flux up to 1 hour ahead by exploiting features derived from the electron flux only. This approach could be helpful to improve monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance.

翻译：近二十年来，对空间天气灾害进行实时监测与预警的需求显著增长。太阳质子事件（SPEs）的预测是空间任务运行与规划面临的最重要挑战之一。在此背景下，人工智能与机器学习技术开辟了新前沿，为统计预报算法提供了新范式。现有绝大多数模型旨在预测SPE的发生，即基于分类方法进行研究。本工作提出一种简单高效的机器学习回归算法，该算法仅利用电子通量衍生的特征，即可对未来1小时内的能量质子通量进行预测。该方法有助于改进深空及近地环境中辐射风险的监测系统。该模型对任务运行与规划具有重要意义，尤其适用于耀斑特征与源位置无法实时获取的场景（例如在火星距离处）。