73 citations as recorded by crossref.

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24. Solar origin of geomagnetic storms and prediction of storms with the use of neural networks H. Lundstedt 10.1007/BF01888978
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26. Twenty-four hour predictions off0F2using time delay neural networks P. Wintoft & L. Cander 10.1029/1998RS002149
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53. Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters M. Stepanova et al. 10.1088/1742-6596/134/1/012041
54. Optimization of the neural-network geomagnetic model for forecasting large-amplitude substorm events V. Gavrishchaka & S. Ganguli 10.1029/2000JA900137
55. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt 10.1029/96JA03068
56. Model Evaluation Guidelines for Geomagnetic Index Predictions M. Liemohn et al. 10.1029/2018SW002067
57. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik 10.3389/fspas.2021.664483
58. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
59. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning A. Gholipour et al. 10.1080/02286203.2004.11442288
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64. 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
65. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt 10.1029/96GL00259
66. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al. 10.1029/2018SW001941
67. Prediction of the Dst Index with Bagging Ensemble-learning Algorithm S. Xu et al. 10.3847/1538-4365/ab880e
68. Multiple-Hour-Ahead Forecast of the Dst Index Using a Combination of Long Short-Term Memory Neural Network and Gaussian Process M. Gruet et al. 10.1029/2018SW001898
69. Prediction of theDstIndex and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al. 10.1029/2018SW001863
70. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. 10.3389/fspas.2020.00039
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73. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov 10.1029/2004GI000065