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Prediction of Ionospheric Scintillation with a Combination of Deep Learning Algorithms Using GNSS Ground-Based Network across South America
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Alireza Atabati   , Iraj Jazireeyan * , Mohammad Mahdi Alizadeh Elizeie |
| K. N. Toosi University of Technology |
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Abstract: (1766 Views) |
Ionospheric plasma disturbances, often caused by solar activity and interplanetary magnetic fields, lead to irregular variations in the electron density of the ionosphere, which result in ionospheric scintillations. The Ionospheric scintillations cause significant fluctuations in the intensity and phase of radio signals that can potentially impact the accuracy of the satellite-based navigation systems. This phenomenon occurs irregularly, and its occurrence rate at low latitudes and near the equatorial anomaly is higher than mid and high latitudes. This study employs a combination of the deep learning network methods and the Huber loss function with observation weighting to predict the spatiotemporal behavior of the ionospheric scintillations in near-real-time. Specifically, the ConvGRU method, which combines a GRU network with a convolutional model, is used for spatiotemporal prediction of the ionospheric scintillation datasets. The data obtained from 121 ground-based GNSS stations, within the latitude range of 15°N to 55°S and longitude range of 270°E to 330°E across the equatorial anomaly region in South America in January, March, and July of the years 2015 (a year of high solar activity) and 2020 (a year of low solar activity) have been utilized. This selection demonstrates the accuracy of the proposed model across a diverse spectrum of geomagnetic scenarios. The designed model achieves regional prediction accuracy of approximately 75% for 2015 and 80% for 2020. This study illustrates that the advanced machine learning techniques can accurately predict the ionospheric scintillations, which can be used near-real-time warning systems to enhance the precision of the observations for the navigation system users.
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| Keywords: Global Navigation Satellite System (GNSS), Ionospheric Irregularities, Deep Learning Algorithms, Convolutional Gated Recurrent Unit (ConvGRU), Huber Loss Function, Amplitude Ionospheric Scintillation (S4) |
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Full-Text [PDF 2857 kb]
(74 Downloads)
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Type of Study: Research |
Subject:
Geodesy
Received: 2024/04/23 | Accepted: 2024/08/3 | ePublished ahead of print: 2025/03/17 | Published: 2025/08/31
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