Investigation of double geomagnetic storms on 3 and 4 February 2022 using machine learning approach | ||
| NRIAG Journal of Astronomy and Geophysics | ||
| Volume 14, Issue 1, December 2025, Pages 1-9 PDF (5.56 M) | ||
| DOI: 10.1080/20909977.2025.2458944 | ||
| Authors | ||
| Mostafa Hegy; Essam Ghamry; Ibrahim El-Hamaly; Sami Abd El Nabi; Ahmad Helaly; Adel Fathy; T.A. Nahool | ||
| Abstract | ||
| The current work utilising machine learning algorithms to investigate the precursor that follows halo coronal mass ejection (CME), eventually leading to moderate geomagnetic storms on the 3 and 4 of February 2022. The methodology involved developing and testing a machine learning model on collected data, implemented with a Gradient Boosting Regressor (GBR) technique. The GBR algorithm demonstrated strong performance, yielding high accuracy and precision over various error metrics. These findings underscore the potential of machine learning methods to effectively estimate geomagnetic storms. Specifically, they position the GBR algorithm as an optimal choice for this prediction task, outperforming other evaluated options. This study provides evidence for the suitability of the GBR regressor for reliable SYM-H index modelling. These results show that geomagnetic storms may be directly predicted from solar wind parameter data, with a lead time of several days for forecasting, which is important for improving space weather forecasts. According to the study, using the GBR regressor model improves the performance to up to 95%. | ||
| Keywords | ||
| Machine Learning Algorithms; forecasting magnetic field; Gradient Boosting Regressor (GBR) regressor; Geomagnetic Storms | ||
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