Model predicts ionospheric scintillation in the Arctic with over 87% accuracy, highlighting machine learning's effectiveness in space weather forecasting.
The ionosphere electron density irregularities cause intense phase scintillation in polar regions, which severely disrupt satellite communication and navigation systems. Reliable scintillation prediction is therefore of critical importance. Using data from multi‐station GNSS receivers, this study integrates Random Forest and Long Short‐Term Memory (LSTM) algorithms to develop a stacked model for scintillation prediction. The rate of TEC Index is identified as the most critical indicator, while parameters like S4 index, TEC, solar radio flux (f10.7), and auroral electrojet upper index (AU) serve as supplementary features with strong correlations to scintillation activity. This proposed model achieves over 87% accuracy in forecasting polar ionospheric phase scintillation events 0.5–3 hr. Comparative analysis shows that the LSTM model outperforms XGBoost in both precision and practicality. This study offers a high‐accuracy model for predicting ionospheric scintillation, which can support space weather alerting systems.
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Zhan et al. (2026) studied this question.
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