Ionospheric Scintillation Forecasting Using Machine Learning
- URL: http://arxiv.org/abs/2409.00118v1
- Date: Wed, 28 Aug 2024 08:21:01 GMT
- Title: Ionospheric Scintillation Forecasting Using Machine Learning
- Authors: Sultan Halawa, Maryam Alansaari, Maryam Sharif, Amel Alhammadi, Ilias Fernini,
- Abstract summary: The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation.
The XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset.
- Score: 0.4369058206183195
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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