An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting

The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accu...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 22; pp. 1 - 5
Main Authors: Wang, Cheng, Xue, Kaiyu, Shi, Chuang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Collocation methods
ConvLSTM model
Data models
Decoding
encoder-decoder structure
Encoders-Decoders
Forecast accuracy
Forecasting
Global navigation satellite system
Ionosphere
Ionospheric forecasting
Long short term memory
Mathematical models
Navigation
Navigation satellites
Navigation systems
Navigational satellites
Optimization
optimized models
Predictive models
Radio communications
Root-mean-square errors
Satellite navigation
Satellite observation
Satellites
Training
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accuracy and stability need improvement. This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance. Using seven years of historical data, the model provides stable forecasts for the following year. Tests from 2015 to 2020 show that optimization reduces root mean square error (RMSE) by 10.159%-16.363% compared to the unoptimized method. The encoder-decoder ConvLSTM-B model achieves the best performance, lowering RMSE by 2.031%-8.547% compared to the ConvLSTM-A model. These results highlight the effectiveness of the proposed approach in improving ionospheric forecast accuracy.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2025.3565645