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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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 12; p. 3064
Main Authors:	Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Shan, Weifeng, Han, Ying, Yuan, Guoming, Cui, Chunjie, Wang, Junling
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2023
Subjects:	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
Online Access:	Get full text
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