66 citations as recorded by crossref.

1. DeepMAD: Deep Learning for Magnetic Anomaly Detection and Denoising X. Xu et al. https://doi.org/10.1109/ACCESS.2020.3006795
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28. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. https://doi.org/10.1029/2019SW002215
29. Features of turbulent cascade development in the magnetosheath during ICME L. Rakhmanova et al. https://doi.org/10.12737/stp-113202502
30. The use of sequential recurrent neural filters in forecasting the Dst index for the strong magnetic storm of autumn 2003 L. Ouarbya et al. https://doi.org/10.1016/j.aml.2011.12.002
31. 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
32. The (un)predictable magnetosphere: the role of the internal dynamics T. Alberti https://doi.org/10.1017/S0022377822000137
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34. Composite Model for Predicting SYM‐H Index Y. Ji et al. https://doi.org/10.1029/2022EA002560
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36. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. https://doi.org/10.1029/2020SW002589
37. Geomagnetic storm forecasting from solar coronal holes S. Nitti et al. https://doi.org/10.1093/mnras/stac3533
38. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al. https://doi.org/10.1038/s41598-022-11721-8
39. 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
40. Prediction of SYM-H index by NARX neural network from IMF and solar wind data L. Cai et al. https://doi.org/10.1007/s11431-009-0296-9
41. Features of turbulent cascade development in the magnetosheath during ICME L. Rakhmanova et al. https://doi.org/10.12737/szf-113202502
42. Potential role of energetic particle observations in geomagnetic storm forecasting D. Ameri & E. Valtonen https://doi.org/10.1016/j.asr.2019.05.012
43. 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
44. 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
45. Forecast of the daily geomagnetic perturbations M. Stodilka https://doi.org/10.15407/knit2010.05.046
46. Zonal geomagnetic indices estimation of the two super geomagnetic activities of 2015 with the artificial neural networks E. Eroglu https://doi.org/10.1016/j.asr.2021.04.036
47. 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
48. 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
49. 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/10.1134/S0016793222600795
50. Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices J. Uwamahoro & J. Habarulema https://doi.org/10.1186/1880-5981-66-95
51. 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
52. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. https://doi.org/10.1007/s11214-017-0412-2
53. 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
54. Which latitudes of the Russian Federation may be affected by extreme magnetic storms? N. Savelieva et al. https://doi.org/10.12737/szf-122202603
55. Analysis of magnetic data of ground station network during strong magnetic storms O. Mandrikova et al. https://doi.org/10.1051/e3sconf/20172002008
56. 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
57. 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
58. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale https://doi.org/10.1029/2018SW002061
59. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. https://doi.org/10.1002/2017SW001608
60. 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
61. 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
62. Prediction of the Dst Geomagnetic Index Using Adaptive Methods I. Myagkova et al. https://doi.org/10.3103/S1068373921030031
63. The Prediction of the Dst-Index Based on Machine Learning Methods A. Efitorov et al. https://doi.org/10.1134/S0010952518060035
64. A mesterséges intelligencia alkalmazásai az űridőjárás vizsgálatában N. Biró & G. Kobán https://doi.org/10.1556/2065.186.2025.8.17
65. Signatures of discrete scale invariance inDsttime series G. Balasis et al. https://doi.org/10.1029/2011GL048019
66. 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