论文标题
使用多保真增强神经网络的多小时DST指数预测
Multi-Hour Ahead Dst Index Prediction Using Multi-Fidelity Boosted Neural Networks
论文作者
论文摘要
干扰风暴时间（DST）指数已被广泛用作环电流强度的代理，因此被用作衡量地磁活性的量度。它是由地磁赤道区域中四个地面磁力计测量得出的。 我们提出了一种新的模型，用于预测$ DST $，交货时间在1到6个小时之间。该模型首先是使用封闭式复发单元（GRU）网络开发的，该网络是使用太阳风参数训练的。然后，通过使用Ackrue方法来估算$ DST $模型的不确定性[Camporeale等。 2021]。最后，为了提高模型的准确性并降低了相关的不确定性，开发了一种多保真提升方法。结果表明，开发的模型可以通过13.54 $ \ mathrm {nt} $的根平方（RMSE）（RMSE）预测$ DST $ 6小时。这比持久性模型和简单的GRU模型要好得多。
The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial regions. We present a new model for predicting $Dst$ with a lead time between 1 and 6 hours. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the $Dst$ model is then estimated by using the ACCRUE method [Camporeale et al. 2021]. Finally, a multi-fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict $Dst$ 6 hours ahead with a root-mean-square-error (RMSE) of 13.54 $\mathrm{nT}$. This is significantly better than the persistence model and a simple GRU model.
