Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC tempora...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 15; číslo 12; s. 3064
Hlavní autoři:	Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Shan, Weifeng, Han, Ying, Yuan, Guoming, Cui, Chunjie, Wang, Junling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.06.2023
Témata:	Accuracy Artificial neural networks Artificial satellites in navigation Coders ConvLSTM Datasets Deep learning East Asia Encoders-Decoders encoder–decoder Environmental aspects geomagnetic storm Global navigation satellite system global positioning systems Ionosphere Ionospheric electron density ionospheric TEC Machine learning Magnetic storms magnetism Modules Neural networks Performance prediction prediction Prediction models Recurrent neural networks Remote sensing Root-mean-square errors Signal processing Solar activity spatiotemporal storms temporal variation Temporal variations Time series time series analysis Total Electron Content Wavelet transforms China East Asia
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
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