72 citations as recorded by crossref.

1. Neural Network Prediction of Monthly Precipitation: Application to Summer Flood Occurrence in Two Regions of Central Europe L. Bodri & V. Čermák https://doi.org/10.1023/A:1021864227759
2. Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al. https://doi.org/10.1029/2018SW001863
3. 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. https://doi.org/10.1029/2018SW001898
4. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov https://doi.org/10.1029/2004GI000065
5. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik https://doi.org/10.3389/fspas.2021.664483
6. Neural network modeling of solar wind‐magnetosphere interaction J. Wu & H. Lundstedt https://doi.org/10.1029/97JA01081
7. A neural network study of the mapping from solar magnetic fields to the daily average solar wind velocity P. Wintoft & H. Lundstedt https://doi.org/10.1029/1998JA900183
8. A neural network prediction of solar cycle 23 A. Conway et al. https://doi.org/10.1029/98JA02539
9. Single particle simulation on the storm-time ring current formation and DST variation Y. Ebihara et al. https://doi.org/10.1016/S0273-1177(02)00784-6
10. Precipitation prediction with neural networks L. Bodri https://doi.org/10.1556/AGeod.36.2001.2.7
11. Nonlinear dynamical-information magnetosphere models for space weather forecasting O. Cheremnykh et al. https://doi.org/10.15407/knit2008.01.077
12. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning A. Gholipour et al. https://doi.org/10.1080/02286203.2004.11442288
13. Auroral electrojet predictions with dynamic neural networks H. Gleisner & H. Lundstedt https://doi.org/10.1029/2001JA900046
14. Prediction of the maximum observed frequency of the ionospheric HF radio channel using the method of artificial neural networks N. Barkhatov et al. https://doi.org/10.1134/S0016793206010099
15. Systems theory for geospace plasma dynamics D. Vassiliadis https://doi.org/10.1029/2004RG000161
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17. Behavior of compressed plasmas in magnetic fields G. Ganguli et al. https://doi.org/10.1007/s41614-020-00048-4
18. Prediction of the Dst Index with Bagging Ensemble-learning Algorithm S. Xu et al. https://doi.org/10.3847/1538-4365/ab880e
19. 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
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21. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al. https://doi.org/10.1002/2016RS006192
22. Dst index estimation from cosmic ray and solar wind data during solar cycle 24 using artificial neural networks H. Sabeha et al. https://doi.org/10.1016/j.jastp.2025.106462
23. Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia L. Bodri & V. Čermák https://doi.org/10.1016/S0965-9978(99)00063-0
24. Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters M. Stepanova et al. https://doi.org/10.1088/1742-6596/134/1/012041
25. Purposeful Prediction of Space Weather Phenomena of Simulated Emotional Learning A. Gholipour et al. https://doi.org/10.2316/Journal.205.2004.2.205-4121
26. Phase shift (time) between storm-time maximum negative excursions of geomagnetic disturbance index Dst and interplanetary Bz R. Kane & E. Echer https://doi.org/10.1016/j.jastp.2007.03.008
27. Optical finite impulse response neural networks P. Silveira et al. https://doi.org/10.1364/AO.41.004162
28. Neural network model for predicting the horizontal component of Earth’s magnetic field (H) over Indian equatorial region during quiet and disturbed periods S. Sajith Babu et al. https://doi.org/10.1016/j.asr.2024.12.014
29. Forecasting of DST variations from polar cap indices using neural networks M. Stepanova et al. https://doi.org/10.1016/j.asr.2003.09.073
30. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions J. Sharifie et al. https://doi.org/10.1029/2005SW000209
31. Forecasting electron precipitation based on predicted geomagnetic activity M. Wüest et al. https://doi.org/10.1016/j.asr.2003.12.014
32. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling J. Sharifi et al. https://doi.org/10.1186/BF03351929
33. Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems H. Lundstedt https://doi.org/10.1016/S0079-1946(97)00187-0
34. Black box modeling of magnetospheric dynamics to forecast geomagnetic activity A. Gholipour et al. https://doi.org/10.1029/2003SW000039
35. Learning based brain emotional intelligence as a new aspect for development of an alarm system T. Babaie et al. https://doi.org/10.1007/s00500-007-0258-8
36. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm S. Kugblenu et al. https://doi.org/10.1186/BF03352234
37. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. https://doi.org/10.1029/2019SW002215
38. Neural network-based nonlinear prediction of magnetic storms D. Jankovičová et al. https://doi.org/10.1016/S1364-6826(02)00025-1
39. An hybrid neuro-wavelet approach for long-term prediction of solar wind C. Napoli et al. https://doi.org/10.1017/S174392131100679X
40. Bézier cubics’ agreement with the neural network of the TEC map E. Eroglu https://doi.org/10.1007/s00500-024-09863-0
41. Short-term prediction of fof2 using time-delay neural network P. Wintoft & L. Cander https://doi.org/10.1016/S1464-1917(99)00009-4
42. Prediction of variations from Polar Cap indices using time-delay neural network M. Stepanova et al. https://doi.org/10.1016/j.jastp.2005.02.027
43. Probabilistic Geomagnetic Storm Forecasting via Deep Learning A. Tasistro‐Hart et al. https://doi.org/10.1029/2020JA028228
44. Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results R. Henwood et al. https://doi.org/10.1007/s11207-009-9419-5
45. Reliable prediction intervals with regression neural networks H. Papadopoulos & H. Haralambous https://doi.org/10.1016/j.neunet.2011.05.008
46. Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach C. Napoli et al. https://doi.org/10.1017/S1743921311006806
47. Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method Y. Li et al. https://doi.org/10.3847/1538-4365/ac616c
48. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu https://doi.org/10.1007/s12517-021-08816-3
49. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale https://doi.org/10.1029/2018SW002061
50. Prediction of daily average solar wind velocity from solar magnetic field observations using hybrid intelligent systems P. Wintoft & H. Lundstedt https://doi.org/10.1016/S0079-1946(97)00186-9
51. 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
52. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks J. Wu & H. Lundstedt https://doi.org/10.1029/97JA00975
53. Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting V. Gavrishchaka & S. Ganguli https://doi.org/10.1029/2001JA900118
54. Twenty‐four hour predictions of f0F2 using time delay neural networks P. Wintoft & L. Cander https://doi.org/10.1029/1998RS002149
55. Control of the current state of geomagnetic activity based on the satellite magnetic measurements in the magnetosphere A. Levitin et al. https://doi.org/10.1134/S0016793210020039
56. Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network A. Bhaskar & G. Vichare https://doi.org/10.1051/swsc/2019007
57. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt https://doi.org/10.1029/96JA03068
58. The Bézier curve and neural network model of the time-domain transient signals E. Eroglu & O. Tretyakov https://doi.org/10.1016/j.cpc.2024.109211
59. 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
60. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al. https://doi.org/10.1007/s11207-024-02340-9
61. The Bézier Curve Modeling of Some Space Climate Parameters Throughout the St. Patrick’s Day Superstorm E. Eroglu et al. https://doi.org/10.1007/s00024-026-04033-9
62. Artificial neural network technique for predicting the critical frequency of the ionospheric F 2 layer N. Barkhatov et al. https://doi.org/10.1007/s11141-005-0043-4
63. Machine learning in orbit estimation: A survey F. Caldas & C. Soares https://doi.org/10.1016/j.actaastro.2024.03.072
64. Comparison of the first four weak and moderate geomagnetic storms of the 2022 using artificial neural networks K. Koklu https://doi.org/10.1016/j.asr.2023.11.029
65. Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events V. Gavrishchaka & S. Ganguli https://doi.org/10.1029/2000JA900137
66. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt https://doi.org/10.1029/96GL00259
67. 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
68. 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
69. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. https://doi.org/10.1002/2017SW001608
70. Space weather modelling with intelligent hybrid systems: Predicting the solar wind velocity P. Wintoft & H. Lundstedt https://doi.org/10.1016/S0273-1177(97)01100-9
71. The bimodal magnetosphere and ring current, radiation belt, and tail transducers R. Sheldon https://doi.org/10.1016/S0273-1177(99)00521-9
72. Discussing Total Electron Content over the Solar Wind Parameters E. Eroglu & T. Muhammad https://doi.org/10.1155/2022/9592008