This study presents the first prediction results of a neural network model for the vertical total electron content of the topside ionosphere based on Swarm-A measurements. The model was trained on 5 years of Swarm-A data over the Euro-African sector spanning the period 1 January 2014 to 31 December 2018. The Swarm-A data was combined with solar and geomagnetic indices to train the NN model. The Swarm-A data of 1 January to 30 September 2019 was used to test the performance of the neural network. The data was divided into two main categories: most quiet and most disturbed days of each month. Each category was subdivided into two sub-categories according to the Swarm-A trajectory i.e. whether it was ascending or descending in order to accommodate the change in local time when the satellite traverses the poles. Four pairs of neural network models were implemented, the first of each pair having one hidden layer, and the second of each pair having two hidden layers, for the following cases: 1) quiet day-ascending, 2) quiet day-descending, 3) disturbed day-ascending, and 4) disturbed day-descending. The topside vertical total electron content predicted by the neural network models compared well with the measurements by Swarm-A. The model that performed best was the one hidden layer model in the case of quiet days for descending trajectories, with RMSE = 1.20 TECU, R = 0.76. The worst performance occurred during the disturbed descending trajectories where the one hidden layer model had the worst RMSE = 2.12 TECU, (R = 0.54), and the two hidden layer model had the worst correlation coefficient R = 0.47 (RMSE = 1.57).In all cases, the neural network models performed better than the IRI2016 model in predicting the topside total electron content. The NN models presented here is the first such attempt at comparing NN models for the topside VTEC based on Swarm-A measurements.

这项研究提出了基于Swarm-A测量的顶侧电离层垂直总电子含量的神经网络模型的第一个预测结果。该模型是基于2014年1月1日至2018年12月31日期间欧洲-非洲地区5年的Swarm-A数据进行训练的。Swarm-A数据与太阳和地磁指数相结合来训练NN模型。使用2019年1月1日至9月30日的Swarm-A数据测试神经网络的性能。数据分为两个主要类别：每个月最安静和最不正常的日子。根据Swarm-A轨迹，将每个类别细分为两个子类别，即是上升还是下降，以便适应卫星穿越两极时的当地时间变化。在以下情况下，实现了四对神经网络模型，每对中的第一对具有一个隐藏层，每对第二对具有两个隐藏层：1）安静的一天上升，2）安静的一天下降，3 ）干扰了一天的上升，以及4）干扰了一天的下降。通过神经网络模型预测的顶部垂直总电子含量与Swarm-A的测量结果进行了比较。表现最佳的模型是在安静的日子中下降轨迹的情况下的一个隐藏层模型，RMSE = 1.20 TECU，R = 0.76。性能最差发生在受干扰的下降轨迹中，其中一个隐藏层模型的最差RMSE = 2.12 TECU（R = 0.54），而两个隐藏层模型的最相关系数R = 0.47（RMSE = 1.57）。在所有情况下 神经网络模型在预测顶部总电子含量方面优于IRI2016模型。此处介绍的NN模型是基于Swarm-A测量结果比较顶部VTEC的NN模型的首次此类尝试。