59 citations as recorded by crossref.

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32. Composite Model for Predicting SYM‐H Index Y. Ji et al. 10.1029/2022EA002560
33. Recognition of geomagnetic storms from time series of matrix observations with the muon hodoscope URAGAN using neural networks of deep learning V. Getmanov et al. 10.12737/szf-101202411
34. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. 10.1029/2020SW002589
35. Geomagnetic storm forecasting from solar coronal holes S. Nitti et al. 10.1093/mnras/stac3533
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48. Investigation of double geomagnetic storms on 3 and 4 February 2022 using machine learning approach M. Hegy et al. 10.1080/20909977.2025.2458944
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51. Analysis of magnetic data of ground station network during strong magnetic storms O. Mandrikova et al. 10.1051/e3sconf/20172002008
52. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. 10.3389/fspas.2020.00039
53. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al. 10.1029/2018SW001941
54. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
55. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. 10.1002/2017SW001608
56. A comparative study of ionospheric IRIEup and ISP assimilative models during some intense and severe geomagnetic storms M. Pietrella et al. 10.1016/j.asr.2018.02.026
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58. The Prediction of the Dst-Index Based on Machine Learning Methods A. Efitorov et al. 10.1134/S0010952518060035
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