Nigusie Ayanew, Tebabal Ambelu, Feyissa Firomsa
Department of Physics, Oda Bultum Univesity, Chiro, Ethiopia.
Washera Geospace and Radar Science Research Laboratory, Bahir Dar University, Bahir Dar, Ethiopia.
Sci Rep. 2024 Aug 20;14(1):19293. doi: 10.1038/s41598-024-69738-0.
Geomagnetic storms can cause variations in the ionization levels of the ionosphere, which is commonly studied using the total electron content (TEC). TEC is a crucial parameter to identify the possible effects of ionospheric variations on satellite communication and navigation. This paper assesses the performance of light gradient boosting machine (LGB) and deep neural network (DNN) machine learning algorithms in modeling ionospheric vertical TEC (VTEC) during geomagnetic disturbances. GPS VTEC data for years 2011-2016 from 13 dual-frequency receiver stations over Ethiopia was utilized. Input parameters for the models were derived from the factors that influence VTEC, such as time, location, geomagnetic activity, solar activity, solar wind, and the interplanetary magnetic field. The LGB model improved the predictions of the DNN model from root mean squared error (RMSE), mean absolute percentage error (MAPE), and R values of 5.45 TECU, 21%, and 0.93 to 4.98 TECU, 18%, and 0.94 on the testing data, respectively. The two machine learning models significantly outperformed the International Reference Ionosphere (IRI 2020) model during the selected geomagnetic storm periods. This study could provide insight into the impacts of ionosphere variations on satellite communication and navigation systems in the low-latitude ionospheric region.
地磁风暴会导致电离层电离水平发生变化，通常使用总电子含量（TEC）来研究电离层。TEC是一个关键参数，用于确定电离层变化对卫星通信和导航可能产生的影响。本文评估了轻梯度提升机（LGB）和深度神经网络（DNN）机器学习算法在地磁扰动期间对电离层垂直总电子含量（VTEC）进行建模的性能。利用了来自埃塞俄比亚13个双频接收站的2011 - 2016年的GPS VTEC数据。模型的输入参数来自影响VTEC的因素，如时间、位置、地磁活动、太阳活动、太阳风以及行星际磁场。在测试数据上，LGB模型将DNN模型的均方根误差（RMSE）、平均绝对百分比误差（MAPE）和R值从5.45 TECU、21%和0.93分别提高到了4.98 TECU、18%和0.94。在选定的地磁风暴期间，这两种机器学习模型的表现明显优于国际参考电离层（IRI 2020）模型。这项研究可以深入了解低纬度电离层区域电离层变化对卫星通信和导航系统的影响。
