Different data-driven prediction of global ionospheric TEC using deep learning methods

Different data-driven prediction of global ionospheric TEC using deep learning methods

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...

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Bibliografische gegevens
Hoofdauteurs: Jun Tang, Mingfei Ding, Dengpan Yang, Cihang Fan, Nasim Khonsari, Wenfei Mao
Formaat: Artikel
Taal:English
Gepubliceerd in: Elsevier 2024-06-01
Reeks:International Journal of Applied Earth Observations and Geoinformation
Onderwerpen:
Ionospheric TEC
Prediction
Random forest
Autoformer
Deep learning
Online toegang:http://www.sciencedirect.com/science/article/pii/S1569843224002437

Internet

http://www.sciencedirect.com/science/article/pii/S1569843224002437

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