Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN
Abstract
:1. Introduction
2. Data and Data Preprocessing
2.1. Data Description
2.2. Data Alignment
2.3. Data Normalization
2.4. Sample Production
3. Methodology
3.1. ST-LSTM
3.2. PredRNN
3.3. Encoder–Decoder Structure
3.4. The Proposed Multichannel ED-PredRNN
4. Experiments Setting
4.1. Evaluation Metrics
4.2. Structure of Comparative Models
4.3. Model Optimization
5. Result and Discussion
5.1. Overall Comparison Under Different Solar Activities
5.2. Comparison at Different Times
5.3. Comparison at Different Spatial Locations
5.4. Single Site Prediction Analysis
5.5. Comparison Under Extreme Situations
6. Conclusions
- The overall comparison between high and low solar activity years shows that Multichannel ED-PredRNN outperforms COPG, ConvLSTM, and ConvGRU. Comparing these three models, the of Multichannel ED-PredRNN decreased by 20.18%, 5.30%, and 10.19% in 2015 (high solar activity), and by 8.34%, 4.87%, and 5.00% in 2019 (low solar activity).
- Comparison at different times indicates that the of each model is linearly positively correlated with the monthly mean of TEC. In all 12 months of 2015 and 8 months of 2019, Multichannel ED-PredRNN performs the best.
- Comparison at different spatial locations shows that Multichannel ED-PredRNN ranks first in all 12 subregions globally.
- The single station prediction using Beijing Station as an example shows that in the vast majority of cases, Multichannel ED-PredRNN outperforms the comparative models, especially at the peaks of TEC.
- The comparison in extreme cases (such as geomagnetic storms) shows that the predictive performance of all models is affected by geomagnetic disturbances, while Multichannel ED-PredRNN is least affected by geomagnetic disturbances.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Set | Training Set | Test Set | ||
---|---|---|---|---|
High Solar Activity (2013, 2014) | Low Solar Activity (2017, 2018) | High Solar Activity (2015) | Low Solar Activity (2019) | |
Number of samples Total | 723 | 723 | 365 | 365 |
1446 | 730 |
Model | Hyper-Parameter Setting | |
---|---|---|
Filter | Kernel Size | |
Multichannel ED-PredRNN | 9 | 9 |
ConvLSTM | 16 | 3 |
ConvGRU | 12 | 3 |
Year and Solar Activity | Model | RMSE (TECU) | R2 | MAPE (%) |
---|---|---|---|---|
2015, high solar activity | COPG | 4.227 | 0.9321 | 19.89 |
ConvLSTM | 3.563 | 0.9518 | 15.32 | |
ConvGRU | 3.757 | 0.9464 | 15.59 | |
Multichannel ED-PredRNN | 3.374 | 0.9567 | 14.22 | |
2019, low solar activity | COPG | 1.618 | 0.9268 | 19.32 |
ConvLSTM | 1.559 | 0.9320 | 17.29 | |
ConvGRU | 1.561 | 0.9319 | 17.96 | |
Multichannel ED-PredRNN | 1.483 | 0.9385 | 15.76 |
Solar Activity | ||
---|---|---|
High (2015) | 96.06% | |
98.71% | ||
90.18% | ||
Low (2019) | 92.07% | |
80.28% | ||
71.87% |
Pearson Correlation Coefficient | COPG | ConvLSTM | ConvGRU | Multichannel ED-PredRNN |
---|---|---|---|---|
2015 | 0.9397 | 0.9517 | 0.9651 | 0.9555 |
2019 | 0.9284 | 0.6921 | 0.5285 | 0.7363 |
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Liu, H.; Ma, Y.; Le, H.; Li, L.; Zhou, R.; Xiao, J.; Shan, W.; Wu, Z.; Li, Y. Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN. Atmosphere 2025, 16, 422. https://doi.org/10.3390/atmos16040422
Liu H, Ma Y, Le H, Li L, Zhou R, Xiao J, Shan W, Wu Z, Li Y. Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN. Atmosphere. 2025; 16(4):422. https://doi.org/10.3390/atmos16040422
Chicago/Turabian StyleLiu, Haijun, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, and Yalan Li. 2025. "Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN" Atmosphere 16, no. 4: 422. https://doi.org/10.3390/atmos16040422
APA StyleLiu, H., Ma, Y., Le, H., Li, L., Zhou, R., Xiao, J., Shan, W., Wu, Z., & Li, Y. (2025). Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN. Atmosphere, 16(4), 422. https://doi.org/10.3390/atmos16040422