Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

中文翻译：

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使用深度学习方法对全球电离层 TEC 进行不同数据驱动的预测


电离层总电子含量（TEC）是监测电离层和空间天气灾害的重要参数。其准确预测对于干涉合成孔径雷达（InSAR）和全球导航卫星系统（GNSS）的精确应用至关重要。本研究提出了一种考虑多个 TEC 相关因素的电离层 TEC 预测新方法。我们提出了随机森林方法和带有多层感知器的 Autoformer 深度学习 (Autoformer-MLP)，通过结合地磁和太阳活动参数来预测全球 TEC。 Autoformer-MLP 执行了两种具有不同电离层数据的方案，即球谐系数 (SHC) 和垂直 TEC (VTEC)。收集从欧洲轨道确定中心（CODE）获得的2009年至2019年（第24周期）的电离层产品，以测试和评估所提出的方法。实验结果表明，2015年和2019年SHC方案预测的全球电离层图（GIM）的均方根误差（RMSE）分别为3.79和1.39 TECU，VTEC方案的均方根误差（RMSE）分别为3.55和1.28 TECU。此外，预测结果的RMSE在太阳活动高和低的年份分别比CODE的1天预测全球电离层图（GIM）产品（C1PG）低0.5和0.2 TECU。这些分析表明，所提出的随机森林和 Autoformer-MLP 深度学习方法在各种场景下的全球电离层 TEC 数据驱动预测中表现出高精度和稳定性。