Abstract

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

中文翻译：

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利用机器学习方法对地球外辐射带中的相对论电子通量进行中短程预测

摘要

研究了提高地球外辐射带相对论电子每小时平均通量和日总通量（通量）时间序列预测质量的方法，分别是提前1-24小时和提前1-4天。预测使用基于各种机器学习方法的近似方法，即人工神经网络 (ANN)、决策树（随机森林）和梯度提升。短期预测的技能分数与 1 到 24 小时的提前期的比较表明，人工神经网络展示了最好的结果。对于中期预测，