Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America
Abstract
:1. Introduction
2. Data Collection
3. Methodology
3.1. Gated Recurrent Unit (GRU)
3.2. Convolutional Gated Recurrent Unit (ConvGRU)
3.3. Loss Function
3.4. ConvGRU Algorithm Architecture
4. Geomagnetic Activities Affecting Ionospheric Scintillation
5. Results and Discussion
5.1. Comparative Analysis of Ionospheric Scintillations
5.2. Quantitative Analysis of the ConvGRU Models Efficacy
5.3. Comparative and Qualitative Analysis
6. Evaluation of Results
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Period of Time | Dst (nT) | Kp | Sunspot Number (SSN) | Solar Flux F10.7 | |||||
---|---|---|---|---|---|---|---|---|---|
Mean | Max | Mean | Max | Mean | Max | Mean | Max | ||
2015 | January | −20.56 | 36 | 1.96 | 6.33 | 93.65 | 153 | 137.32 | 171.80 |
March | −27.75 | 45 | 2.46 | 7.66 | 54.18 | 108 | 126.29 | 145.60 | |
July | −19.43 | 28 | 1.71 | 5.66 | 65.78 | 123 | 106.91 | 133.40 | |
2020 | January | −3.63 | 21 | 1.02 | 3.66 | 6.18 | 19 | 72.23 | 74.70 |
March | −5.76 | 12 | 1.34 | 4.33 | 1.84 | 16 | 70.12 | 72.10 | |
July | −4.73 | 22 | 0.96 | 4 | 6.68 | 26 | 69.57 | 73.30 |
Period of Time | 2015 | 2020 | ||||||
---|---|---|---|---|---|---|---|---|
January | March | July | January | March | July | |||
Ionospheric conditions | Mean VTEC (TECU) | 23.31 | 29.26 | 12.36 | 9.26 | 11.81 | 7.77 | |
Mean S4 | 0.152 | 0.148 | 0.147 | 0.130 | 0.128 | 0.130 | ||
Scintillation Occurrence (S4 > 0.2) | 19.32% | 17.46% | 17.09% | 10.55% | 11.14% | 11.50% | ||
ConvGRU-LC | Modeling | CC | 0.91 | 0.91 | 0.92 | 0.94 | 0.92 | 0.92 |
RMSE | 0.008 | 0.009 | 0.009 | 0.006 | 0.007 | 0.007 | ||
Mean Error (ME) | 0.019 | 0.017 | 0.016 | 0.011 | 0.013 | 0.014 | ||
Prediction | CC | 0.80 | 0.80 | 0.81 | 0.83 | 0.82 | 0.85 | |
RMSE | 0.024 | 0.024 | 0.022 | 0.013 | 0.016 | 0.016 | ||
Mean Error (ME) | 0.04 | 0.04 | 0.03 | 0.02 | 0.02 | 0.02 | ||
ConvGRU-Llog-cosh | Modeling | CC | 0.88 | 0.89 | 0.89 | 0.91 | 0.90 | 0.90 |
RMSE | 0.013 | 0.013 | 0.012 | 0.010 | 0.010 | 0.011 | ||
Mean Error (ME) | 0.025 | 0.023 | 0.022 | 0.019 | 0.020 | 0.021 | ||
Prediction | CC | 0.77 | 0.78 | 0.78 | 0.81 | 0.80 | 0.83 | |
RMSE | 0.039 | 0.035 | 0.030 | 0.022 | 0.023 | 0.025 | ||
Mean Error (ME) | 0.06 | 0.05 | 0.04 | 0.03 | 0.03 | 0.03 | ||
ConvGRU-Lδ | Modeling | CC | 0.86 | 0.86 | 0.87 | 0.92 | 0.91 | 0.90 |
RMSE | 0.014 | 0.014 | 0.013 | 0.009 | 0.010 | 0.010 | ||
Mean Error (ME) | 0.026 | 0.025 | 0.024 | 0.018 | 0.019 | 0.021 | ||
Prediction | CC | 0.75 | 0.75 | 0.76 | 0.80 | 0.79 | 0.81 | |
RMSE | 0.042 | 0.038 | 0.032 | 0.020 | 0.019 | 0.023 | ||
Mean Error (ME) | 0.07 | 0.06 | 0.05 | 0.04 | 0.04 | 004 |
Period of Time | 2015 | 2020 | |||||
---|---|---|---|---|---|---|---|
January | March | July | January | March | July | ||
ConvGRU-LC | FAR | 0.34 | 0.32 | 0.28 | 0.19 | 0.21 | 0.16 |
CSI | 0.43 | 0.45 | 0.52 | 0.64 | 0.66 | 0.70 | |
POD | 0.59 | 0.61 | 0.65 | 0.80 | 0.81 | 0.83 | |
TSS | 0.53 | 0.55 | 0.58 | 0.72 | 0.73 | 0.76 | |
ConvGRU-Llog-cosh | FAR | 0.36 | 0.34 | 0.30 | 0.20 | 0.22 | 0.17 |
CSI | 0.41 | 0.44 | 0.50 | 0.62 | 0.64 | 0.68 | |
POD | 0.56 | 0.59 | 0.62 | 0.78 | 0.79 | 0.81 | |
TSS | 0.51 | 0.53 | 0.55 | 0.70 | 0.71 | 0.74 | |
ConvGRU-Lδ | FAR | 0.37 | 0.35 | 0.32 | 0.21 | 0.23 | 0.18 |
CSI | 0.40 | 0.42 | 0.47 | 0.61 | 0.63 | 0.66 | |
POD | 0.55 | 0.57 | 0.58 | 0.76 | 0.77 | 0.79 | |
TSS | 0.49 | 0.51 | 0.54 | 0.69 | 0.70 | 0.72 |
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Atabati, A.; Jazireeyan, I.; Alizadeh, M.; Langley, R.B. Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America. Remote Sens. 2024, 16, 2757. https://doi.org/10.3390/rs16152757
Atabati A, Jazireeyan I, Alizadeh M, Langley RB. Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America. Remote Sensing. 2024; 16(15):2757. https://doi.org/10.3390/rs16152757
Chicago/Turabian StyleAtabati, Alireza, Iraj Jazireeyan, Mahdi Alizadeh, and Richard B. Langley. 2024. "Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America" Remote Sensing 16, no. 15: 2757. https://doi.org/10.3390/rs16152757
APA StyleAtabati, A., Jazireeyan, I., Alizadeh, M., & Langley, R. B. (2024). Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America. Remote Sensing, 16(15), 2757. https://doi.org/10.3390/rs16152757