Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica
Abstract
:1. Introduction
2. Data and Methodology
2.1. Data Source
2.2. Method for Determining ZTD
2.2.1. GPT3_ZTD
2.2.2. LSTM_ZTD
2.2.3. LSTM_RBF_ZTD
2.3. Verification
3. Results
3.1. Accuracy of the LSTM_ZTD
3.2. Comparisons between the LSTM_RBF_ZTD and the GPT3_ZTD
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Daily RMSE | 0–5 mm | 5–10 mm | >10 mm |
---|---|---|---|
PRPT | 26 | 104 | 222 |
THU4 | 70 | 143 | 152 |
MCM4 | 115 | 166 | 84 |
AMU2 | 193 | 133 | 39 |
DAV1 | 73 | 150 | 130 |
mean | 95.4 | 139.2 | 125.4 |
RMSE | Number of sites | |
---|---|---|
Yearly RMSE | 5–10 mm | 40 |
10–15 mm | 14 | |
15–20 mm | 9 | |
>20 mm | 2 | |
Daily RMSE | 50% < 10 mm | 50 |
75% < 10 mm | 35 | |
50% < 20 mm | 63 | |
75% < 20 mm | 63 |
Daily RMSE | LSTM_RBF_ZTD | GPT3_ZTD |
---|---|---|
50% < 10 mm | 31 | 0 |
75% < 10 mm | 8 | 0 |
50% < 20 mm | 62 | 39 |
75% < 20 mm | 47 | 2 |
Yearly RMSE | LSTM_RBF_ZTD | GPT3_ZTD |
---|---|---|
0–10 mm | 12 | 0 |
10–20 mm | 40 | 8 |
20–30 mm | 10 | 41 |
>30 mm | 3 | 16 |
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Li, S.; Xu, T.; Xu, Y.; Jiang, N.; Bastos, L. Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica. Atmosphere 2022, 13, 78. https://doi.org/10.3390/atmos13010078
Li S, Xu T, Xu Y, Jiang N, Bastos L. Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica. Atmosphere. 2022; 13(1):78. https://doi.org/10.3390/atmos13010078
Chicago/Turabian StyleLi, Song, Tianhe Xu, Yan Xu, Nan Jiang, and Luísa Bastos. 2022. "Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica" Atmosphere 13, no. 1: 78. https://doi.org/10.3390/atmos13010078
APA StyleLi, S., Xu, T., Xu, Y., Jiang, N., & Bastos, L. (2022). Forecasting GNSS Zenith Troposphere Delay by Improving GPT3 Model with Machine Learning in Antarctica. Atmosphere, 13(1), 78. https://doi.org/10.3390/atmos13010078