64 citations as recorded by crossref.

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30. Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis M. Jawad et al. https://doi.org/10.1109/TPS.2018.2887202
31. The (un)predictable magnetosphere: the role of the internal dynamics T. Alberti https://doi.org/10.1017/S0022377822000137
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36. Geomagnetic storm forecasting from solar coronal holes S. Nitti et al. https://doi.org/10.1093/mnras/stac3533
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38. Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment J. Watermann et al. https://doi.org/10.1007/s11214-009-9494-9
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41. Potential role of energetic particle observations in geomagnetic storm forecasting D. Ameri & E. Valtonen https://doi.org/10.1016/j.asr.2019.05.012
42. Forecasting the State of the Earth’s Magnetosphere Using a Special Algorithm for Working with Multidimensional Time Series R. Vladimirov et al. https://doi.org/10.3103/S0027134924702266
43. A Method for Forecasting Geomagnetic Storms Based on Deep Learning Neural Networks Using Time Series of Matrix Observations of the Uragan Muon Hodoscope V. Getmanov et al. https://doi.org/10.1134/S0016793224600644
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46. Using artificial neural networks in the investigation of four moderate geomagnetic storms (mGSs) that occurred in 2015 F. Basciftci https://doi.org/10.1016/j.asr.2023.01.001
47. Comparison of the Efficiency of Machine Learning Methods in Studying the Importance of Input Features in the Problem of Forecasting the Dst Geomagnetic Index R. Vladimirov et al. https://doi.org/10.31857/S0016794022100224
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50. Investigation of double geomagnetic storms on 3 and 4 February 2022 using machine learning approach M. Hegy et al. https://doi.org/10.1080/20909977.2025.2458944
51. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. https://doi.org/10.1007/s11214-017-0412-2
52. Comparison between three algorithms for Dst predictions over the 2003–2005 period E. Amata et al. https://doi.org/10.1016/j.jastp.2007.08.041
53. Analysis of magnetic data of ground station network during strong magnetic storms O. Mandrikova et al. https://doi.org/10.1051/e3sconf/20172002008
54. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. https://doi.org/10.3389/fspas.2020.00039
55. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al. https://doi.org/10.1029/2018SW001941
56. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale https://doi.org/10.1029/2018SW002061
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58. Examining the Super Intense Geomagnetic Storm on 10–11 May, 2024 via Artificial Neural Networks S. Bulbul et al. https://doi.org/10.3390/atmos17030302
59. A comparative study of ionospheric IRIEup and ISP assimilative models during some intense and severe geomagnetic storms M. Pietrella et al. https://doi.org/10.1016/j.asr.2018.02.026
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61. The Prediction of the Dst-Index Based on Machine Learning Methods A. Efitorov et al. https://doi.org/10.1134/S0010952518060035
62. A mesterséges intelligencia alkalmazásai az űridőjárás vizsgálatában N. Biró & G. Kobán https://doi.org/10.1556/2065.186.2025.8.17
63. Signatures of discrete scale invariance inDsttime series G. Balasis et al. https://doi.org/10.1029/2011GL048019
64. Mission analysis for the HENON CubeSat mission to a large Sun-Earth distant retrograde orbit S. Cicalò et al. https://doi.org/10.1007/s10509-025-04473-0