87 citations as recorded by crossref.

1. Artificial neural network technique for predicting the critical frequency of the ionospheric F 2 layer N. Barkhatov et al. 10.1007/s11141-005-0043-4
2. Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results R. Henwood et al. 10.1007/s11207-009-9419-5
3. Prediction of the Dst index from solar wind parameters by a neural network method S. Watanabe et al. 10.1186/BF03352454
4. 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
5. Prediction of the maximum observed frequency of the ionospheric HF radio channel using the method of artificial neural networks N. Barkhatov et al. 10.1134/S0016793206010099
6. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm S. Kugblenu et al. 10.1186/BF03352234
7. Solar origin of geomagnetic storms and predictions H. Lundstedt 10.1016/0021-9169(95)00105-0
8. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. 10.1007/s11214-017-0412-2
9. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al. 10.1038/s41598-022-11721-8
10. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu 10.1007/s12517-021-08816-3
11. Prediction of intensity of moderate and intense geomagnetic storms using artificial neural network during two complete solar cycles 23 and 24 P. Singh 10.1007/s12648-021-02192-0
12. Short-term prediction of fof2 using time-delay neural network P. Wintoft & L. Cander 10.1016/S1464-1917(99)00009-4
13. Prediction of solar and geomagnetic activity data using neural networks K. Macpherson et al. 10.1029/95JA02283
14. 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
15. Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method Y. Li et al. 10.3847/1538-4365/ac616c
16. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. 10.1029/2020SW002589
17. Prediction of horizontal component of earth's magnetic field over Indian sector using neural network model K. Unnikrishnan 10.1016/j.jastp.2014.06.014
18. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al. 10.1002/2016RS006192
19. Complexity Heliophysics: A Lived and Living History of Systems and Complexity Science in Heliophysics R. McGranaghan 10.1007/s11214-024-01081-2
20. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions J. Sharifie et al. 10.1029/2005SW000209
21. Auroral electrojet predictions with dynamic neural networks H. Gleisner & H. Lundstedt 10.1029/2001JA900046
22. What sustained multi-disciplinary research can achieve: The space weather modeling framework T. Gombosi et al. 10.1051/swsc/2021020
23. Neural network modeling of solar wind‐magnetosphere interaction J. Wu & H. Lundstedt 10.1029/97JA01081
24. Incoherent radar spectrum processing using neural network R. Mueller et al. 10.1016/S0165-1684(98)00094-2
25. An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning R. Lin 林 et al. 10.3847/1538-4365/ad2dea
26. On the relation of the AE and PC indices J. Takalo & J. Timonen 10.1029/98JA02390
27. 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 10.1051/swsc/2019007
28. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. 10.1002/2017SW001608
29. A comparison of machine-learning techniques for the prediction of the auroral electrojet index S. Gopinath & P. Prince 10.1007/s12040-019-1194-6
30. Transit clairvoyance: enhancingTESSfollow-up using artificial neural networks D. Kipping & C. Lam 10.1093/mnras/stw2974
31. Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events V. Gavrishchaka & S. Ganguli 10.1029/2000JA900137
32. Investigating and comparing the two superstorms in the 23rd solar cycle F. Basciftci 10.1007/s12648-022-02396-y
33. Energy as a tracer of magnetospheric processes: GUMICS-4 global MHD results and observations compared T. Pulkkinen et al. 10.1016/j.jastp.2007.10.011
34. 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
35. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al. 10.1029/2018SW001941
36. Prediction of the Dst Index with Bagging Ensemble-learning Algorithm S. Xu et al. 10.3847/1538-4365/ab880e
37. 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
38. Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al. 10.1029/2018SW001863
39. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al. 10.1007/s11207-024-02340-9
40. A neural network approach to the classification of electron and proton whistlers X. Miniere et al. 10.1016/0021-9169(95)00077-1
41. Forecasting of DST variations from polar cap indices using neural networks M. Stepanova et al. 10.1016/j.asr.2003.09.073
42. Forecasting of ionospheric critical frequency using neural networks O. Altinay et al. 10.1029/97GL01381
43. ANN-Based Assessment of the Influence of Natural and Anthropogenic Forcing on Surface Air Temperature Variability Over the Indian Subcontinent B. Padmavathi et al. 10.1007/s00024-021-02724-z
44. Prediction of variations from Polar Cap indices using time-delay neural network M. Stepanova et al. 10.1016/j.jastp.2005.02.027
45. Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps J. Tian et al. 10.1016/j.asr.2004.06.017
46. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. 10.1029/2019SW002215
47. Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems H. Lundstedt 10.1016/S0079-1946(97)00187-0
48. Progress in space weather predictions and applications H. Lundstedt 10.1016/j.asr.2003.09.072
49. An operational approach to forecast the Earth’s radiation belts dynamics G. Bernoux et al. 10.1051/swsc/2021045
50. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks J. Wu & H. Lundstedt 10.1029/97JA00975
51. Purposeful Prediction of Space Weather Phenomena of Simulated Emotional Learning A. Gholipour et al. 10.2316/Journal.205.2004.2.205-4121
52. Learning based brain emotional intelligence as a new aspect for development of an alarm system T. Babaie et al. 10.1007/s00500-007-0258-8
53. Black box modeling of magnetospheric dynamics to forecast geomagnetic activity A. Gholipour et al. 10.1029/2003SW000039
54. RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics M. Liemohn et al. 10.1016/j.jastp.2021.105624
55. <i>Dst</i> Index Prediction Method Based on LSTM Neural Network S. LI et al. 10.11728/cjss2025.03.2024-0045
56. 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. 10.1016/j.asr.2024.12.014
57. On the application of information theory to the optimum state-space reconstruction of the short-term solar radio flux (10.7 cm), and its prediction via a neural network T. Chatterjee 10.1046/j.1365-8711.2001.04110.x
58. Solar origin of geomagnetic storms and prediction of storms with the use of neural networks H. Lundstedt 10.1007/BF01888978
59. Daily geomagnetic field prediction of INTERMAGNET observatories data using the multilayer perceptron neural network S. Ouadfeul et al. 10.1007/s12517-014-1308-z
60. Twenty‐four hour predictions of f0F2 using time delay neural networks P. Wintoft & L. Cander 10.1029/1998RS002149
61. Operational models for forecasting Dst S. Watanabe et al. 10.1016/S0273-1177(02)00809-8
62. Forecasting ionospheric structure during the great geomagnetic storms L. Cander & S. Mihajlovic 10.1029/97JA02418
63. Cosmic ray variability and geomagnetic activity: A statistical study K. Kudela & M. Storini 10.1016/j.jastp.2005.02.018
64. A neural network study of the mapping from solar magnetic fields to the daily average solar wind velocity P. Wintoft & H. Lundstedt 10.1029/1998JA900183
65. Neural network-based nonlinear prediction of magnetic storms D. Jankovičová et al. 10.1016/S1364-6826(02)00025-1
66. Time series, neural networks and the future of the Sun A. Conway 10.1016/S1387-6473(98)00041-4
67. Neural network prediction of AE data J. Takalo & J. Timonen 10.1029/97GL02457
68. A cellular automata-based model of Earth's magnetosphere in relation withDstindex A. Banerjee et al. 10.1002/2014SW001138
69. 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
70. Operational forecasts of the geomagnetic Dst index H. Lundstedt et al. 10.1029/2002GL016151
71. Comparison of neural network and support vector machine methods for Kp forecasting E. Ji et al. 10.1002/jgra.50500
72. Automatic detection of sudden commencements using neural networks A. Segarra & J. Curto 10.5047/eps.2012.12.011
73. 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
74. Prediction of Partial Ring Current Index Using LSTM Neural Network H. LI et al. 10.11728/cjss2022.05.210513061
75. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt 10.1029/96JA03068
76. Model Evaluation Guidelines for Geomagnetic Index Predictions M. Liemohn et al. 10.1029/2018SW002067
77. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik 10.3389/fspas.2021.664483
78. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
79. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning A. Gholipour et al. 10.1080/02286203.2004.11442288
80. A neural network prediction of solar cycle 23 A. Conway et al. 10.1029/98JA02539
81. Operational Dst index prediction model based on combination of artificial neural network and empirical model W. Park et al. 10.1051/swsc/2021021
82. Space weather modelling with intelligent hybrid systems: Predicting the solar wind velocity P. Wintoft & H. Lundstedt 10.1016/S0273-1177(97)01100-9
83. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt 10.1029/96GL00259
84. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. 10.3389/fspas.2020.00039
85. Neural network prediction of the AE index from the PC index J. Takalo & J. Timonen 10.1016/S1464-1917(98)00013-0
86. 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
87. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov 10.1029/2004GI000065