In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ∼0.21 and 0.82, respectively. The most important parameter for these predictions was found to be the minimum observed B_Z. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, for example, from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ∼ 0.16, ROC Scores ∼ 0.8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations.

中文翻译：

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基于机器学习的行星际冲击风暴突然开始的概率预测

在这项研究中，我们研究了几种不同的机器学习模型提供概率预测的能力，这些预测是关于在L1处地球上游观测到的行星际冲击是否会导致即刻（突然爆发，SC）或更长时间的磁层活动（风暴突然爆发，SSC） ）。四个模型中测试，包括直链（逻辑回归），非线性（朴素贝叶斯和高斯过程），和合奏（随机森林）模型和被示出为用户提供的布来技能分数（的BSS）的SC的熟练和可靠的预测〜 0.3和ROC分数>0.8。发现最有力的预测参数是行星际磁场中的范围。该模型还可以生成SSC的熟练预测，尽管其可靠性低于SC的可靠性。返回的BSS和ROC分数分别约为0.21和0.82。发现这些预测的最重要参数是观测到的最小B _Z。通过包括与磁层指数相关的其他功能及其在冲击过程中的变化，对冲击的简单参数化进行了测试，从而使可靠性得到适度提高。几个参数，例如速度和密度，可能可以在更长的交货时间内被更准确地预测，例如，来自日球影像。当输入受到速度和密度的限制时，尽管可靠性比以前低（BSS约为 0.16，ROC得分约为 0.8），但模型在预测SSC方面表现良好，最后，使用超出当前水平的假设极端数据对模型进行了测试。观察结果，显示出截然不同的外推法。