63 citations as recorded by crossref.

1. DeepMAD: Deep Learning for Magnetic Anomaly Detection and Denoising X. Xu et al. 10.1109/ACCESS.2020.3006795
2. Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al. 10.1029/2018SW001863
3. A machine learning approach for automated ULF wave recognition G. Balasis et al. 10.1051/swsc/2019010
4. Role of the variable solar wind in the dynamics of small-scale magnetosheath structures L. Rakhmanova et al. 10.3389/fspas.2023.1121230
5. 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/stp-101202411
6. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik 10.3389/fspas.2021.664483
7. A study on precursors leading to geomagnetic storms using artificial neural network G. SINGH & A. SINGH 10.1007/s12040-016-0702-1
8. Recognition of Geomagnetic Storm Based on Neural Network Model Estimates of Dst Indices A. Belov et al. 10.1134/S106423072201004X
9. Forecasting of global data‐binning parameters for high‐resolution empirical geomagnetic field models M. Sitnov et al. 10.1029/2012SW000783
10. AE index forecast at different time scales through an ANN algorithm based on L1 IMF and plasma measurements G. Pallocchia et al. 10.1016/j.jastp.2007.08.038
11. Statistical Prediction of Dst Index by Solar Wind Data and <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-Distributions P. Qin & R. Nishii 10.1109/TPS.2015.2485661
12. Analysis of geomagnetic hourly ranges D. Danskin & S. Lotz 10.1002/2015SW001184
13. The Use of Coupling Functions in the Forecasting of the Dst-Index Amplitude with Adaptive Methods I. Myagkova et al. 10.1134/S0016793220060092
14. Measuring Information Coupling between the Solar Wind and the Magnetosphere–Ionosphere System M. Stumpo et al. 10.3390/e22030276
15. Evolution of Dynamical Complexities in Geospace as Captured by Dst Over Four Solar Cycles 1964–2008. S. Ogunjo et al. 10.1029/2020JA027873
16. On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index A. Banerjee et al. 10.1007/s10509-011-0836-1
17. Auroral ultraviolet images for prediction of the auroral electrojet index J. Li et al. 10.1007/s11600-025-01589-3
18. Geomagnetic storm forecasting service StormFocus: 5 years online T. Podladchikova et al. 10.1051/swsc/2018017
19. Comparison of Dst forecast models for intense geomagnetic storms E. Ji et al. 10.1029/2011JA016872
20. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu 10.1007/s12517-021-08816-3
21. Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach M. Chandorkar et al. 10.1002/2017SW001627
22. Geomagnetic Storm Prediction Based on the Neural Network Digital Processing of Joint Observations of the URAGAN Muon Hodoscope and Neutron Monitor Stations V. Getmanov et al. 10.1134/S0016793222040089
23. Probabilistic Geomagnetic Storm Forecasting via Deep Learning A. Tasistro‐Hart et al. 10.1029/2020JA028228
24. An empirical model of the Earth’s bow shock based on an artificial neural network G. Pallocchia & L. Trenchi 10.1016/j.pss.2021.105196
25. Dst index estimation from cosmic ray and solar wind data during solar cycle 24 using artificial neural networks H. Sabeha et al. 10.1016/j.jastp.2025.106462
26. A cellular automata-based model of Earth's magnetosphere in relation withDstindex A. Banerjee et al. 10.1002/2014SW001138
27. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. 10.1029/2019SW002215
28. Features of turbulent cascade development in the magnetosheath during ICME L. Rakhmanova et al. 10.12737/stp-113202502
29. The use of sequential recurrent neural filters in forecasting the Dst index for the strong magnetic storm of autumn 2003 L. Ouarbya et al. 10.1016/j.aml.2011.12.002
30. Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis M. Jawad et al. 10.1109/TPS.2018.2887202
31. The (un)predictable magnetosphere: the role of the internal dynamics T. Alberti 10.1017/S0022377822000137
32. Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks J. Uwamahoro et al. 10.5194/angeo-30-963-2012
33. Composite Model for Predicting SYM‐H Index Y. Ji et al. 10.1029/2022EA002560
34. 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
35. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. 10.1029/2020SW002589
36. Geomagnetic storm forecasting from solar coronal holes S. Nitti et al. 10.1093/mnras/stac3533
37. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al. 10.1038/s41598-022-11721-8
38. Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment J. Watermann et al. 10.1007/s11214-009-9494-9
39. Prediction of SYM-H index by NARX neural network from IMF and solar wind data L. Cai et al. 10.1007/s11431-009-0296-9
40. Features of turbulent cascade development in the magnetosheath during ICME L. Rakhmanova et al. 10.12737/szf-113202502
41. Potential role of energetic particle observations in geomagnetic storm forecasting D. Ameri & E. Valtonen 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. 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. 10.1134/S0016793224600644
44. Forecast of the daily geomagnetic perturbations M. Stodilka 10.15407/knit2010.05.046
45. Zonal geomagnetic indices estimation of the two super geomagnetic activities of 2015 with the artificial neural networks E. Eroglu 10.1016/j.asr.2021.04.036
46. Using artificial neural networks in the investigation of four moderate geomagnetic storms (mGSs) that occurred in 2015 F. Basciftci 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. 10.31857/S0016794022100224
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. 10.1134/S0016793222600795
49. Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices J. Uwamahoro & J. Habarulema 10.1186/1880-5981-66-95
50. Investigation of double geomagnetic storms on 3 and 4 February 2022 using machine learning approach M. Hegy et al. 10.1080/20909977.2025.2458944
51. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. 10.1007/s11214-017-0412-2
52. Comparison between three algorithms for Dst predictions over the 2003–2005 period E. Amata et al. 10.1016/j.jastp.2007.08.041
53. Analysis of magnetic data of ground station network during strong magnetic storms O. Mandrikova et al. 10.1051/e3sconf/20172002008
54. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. 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. 10.1029/2018SW001941
56. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
57. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. 10.1002/2017SW001608
58. 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
59. Prediction of the Dst Geomagnetic Index Using Adaptive Methods I. Myagkova et al. 10.3103/S1068373921030031
60. The Prediction of the Dst-Index Based on Machine Learning Methods A. Efitorov et al. 10.1134/S0010952518060035
61. A mesterséges intelligencia alkalmazásai az űridőjárás vizsgálatában N. Biró & G. Kobán 10.1556/2065.186.2025.8.17
62. Signatures of discrete scale invariance inDsttime series G. Balasis et al. 10.1029/2011GL048019
63. Mission analysis for the HENON CubeSat mission to a large Sun-Earth distant retrograde orbit S. Cicalò et al. 10.1007/s10509-025-04473-0