Ionospheric Scintillation Forecasting Using Machine Learning
<|reference_start|>Ionospheric Scintillation Forecasting Using 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.<|reference_end|>
@article{halawa2024ionospheric,
    title={Ionospheric Scintillation Forecasting Using Machine Learning},
    author={Sultan Halawa, Maryam Alansaari, Maryam Sharif, Amel Alhammadi, Ilias
  Fernini},
    journal={arXiv preprint arXiv:2409.00118},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00118},
    primaryClass={eess.SP cs.LG stat.ML}
}