Ionospheric Scintillation Forecasting Using Machine Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914931640631296 |
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| author | Halawa, Sultan Alansaari, Maryam Sharif, Maryam Alhammadi, Amel Fernini, Ilias |
| author_facet | Halawa, Sultan Alansaari, Maryam Sharif, Maryam Alhammadi, Amel Fernini, Ilias |
| contents | This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause fluctuations in GNSS signal power. These fluctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ionospheric Scintillation Forecasting Using Machine Learning Halawa, Sultan Alansaari, Maryam Sharif, Maryam Alhammadi, Amel Fernini, Ilias Signal Processing Machine Learning This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause fluctuations in GNSS signal power. These fluctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity. |
| title | Ionospheric Scintillation Forecasting Using Machine Learning |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2409.00118 |