To find the best method of predicting when daily relativistic electron flux (>2 MeV) will rise at geosynchronous orbit, we compare model predictive success rates (true positive rate or TPR) for multiple regression, ARMAX, logistic regression, a feed‐forward multilayer perceptron (MLP), and a recurrent neural network (RNN) model. We use only those days on which flux could rise, removing days when flux is already high from the data set. We explore three input variable sets: (1) ground‐based data (Kp, Dst, and sunspot number), (2) a full set of easily available solar wind and interplanetary magnetic field parameters (|B|, Bz, V, N, P, Ey, Kp, Dst, and sunspot number, and (3) this full set with the addition of previous day's flux. Despite high validation correlations in the multiple regression and ARMAX predictions, these regression models had low predictive ability (TPR < 45%) and are not recommended for use. The three classifier model types (logistic regression, MLP, and RNN) performed better (TPR: 50.8–74.6%). These rates were increased further if the cost of missing an event was set at 4 times that of predicting an event that did not happen (TPR: 73.1–89.6%). The area under the receiver operating characteristic curves did not, for the most part, differ between the classifier models (logistic, MLP, and RNN), indicating that any of the three could be used to discriminate between events and nonevents, but validation suggests a full RNN model performs best. The addition of previous day's flux as a predictor provided only a slight advantage.

中文翻译：

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分类器神经网络模型比多重回归或ARMAX模型更好地预测地球同步轨道上的相对论电子事件

为了找到预测何时在地球同步轨道上每日相对论电子通量（> 2 MeV）上升的最佳方法，我们比较了多元回归，ARMAX，对数回归，前馈多层模型的模型预测成功率（真实正率或TPR）感知器（MLP）和递归神经网络（RNN）模型。我们仅使用流量可能增加的那些日子，而从数据集中删除流量已经很高的日子。我们探索了三个输入变量集：（1）地面数据（Kp，Dst和黑子数），（2）全套易于获得的太阳风和行星际磁场参数（| B |，Bz，V和N，P，Ey，Kp，Dst，太阳黑子数，以及（3）加上前一天的流量后的全套。尽管在多元回归和ARMAX预测中具有较高的验证相关性，但这些回归模型的预测能力较低（TPR <45％），不建议使用。三种分类器模型类型（逻辑回归，MLP和RNN）表现更好（TPR：50.8–74.6％）。如果将丢失事件的成本设置为预测未发生事件的成本的4倍，则这些比率会进一步提高（TPR：73.1–89.6％）。接收器工作特性曲线下的区域在分类器模型（逻辑模型，MLP和RNN）之间大部分没有差异，表明这三个模型中的任何一个都可以用来区分事件和非事件，但是验证表明完整的RNN模型表现最佳。