90 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.
2. Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results R. Henwood et al.
3. Prediction of the Dst index from solar wind parameters by a neural network method S. Watanabe et al.
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.
5. Prediction of the maximum observed frequency of the ionospheric HF radio channel using the method of artificial neural networks N. Barkhatov et al.
6. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm S. Kugblenu et al.
7. Solar origin of geomagnetic storms and predictions H. Lundstedt
8. Prediction of Surface Air Temperatures by Neural Network, Example Based on Three-Year Temperature Monitoring at Spořilov Station L. Bodri & V. Čermák
9. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al.
10. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al.
11. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu
12. Prediction of intensity of moderate and intense geomagnetic storms using artificial neural network during two complete solar cycles 23 and 24 P. Singh
13. Short-term prediction of fof2 using time-delay neural network P. Wintoft & L. Cander
14. Prediction of solar and geomagnetic activity data using neural networks K. Macpherson et al.
15. Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks J. Uwamahoro et al.
16. Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method Y. Li et al.
17. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al.
18. Prediction of horizontal component of earth's magnetic field over Indian sector using neural network model K. Unnikrishnan
19. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al.
20. Complexity Heliophysics: A Lived and Living History of Systems and Complexity Science in Heliophysics R. McGranaghan
21. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions J. Sharifie et al.
22. Auroral electrojet predictions with dynamic neural networks H. Gleisner & H. Lundstedt
23. What sustained multi-disciplinary research can achieve: The space weather modeling framework T. Gombosi et al.
24. Neural network modeling of solar wind‐magnetosphere interaction J. Wu & H. Lundstedt
25. Incoherent radar spectrum processing using neural network R. Mueller et al.
26. An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning R. Lin 林 et al.
27. On the relation of the AE and PC indices J. Takalo & J. Timonen
28. 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
29. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al.
30. A comparison of machine-learning techniques for the prediction of the auroral electrojet index S. Gopinath & P. Prince
31. Transit clairvoyance: enhancingTESSfollow-up using artificial neural networks D. Kipping & C. Lam
32. Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events V. Gavrishchaka & S. Ganguli
33. Probabilistic geomagnetic storm prediction for operational forecasting S. Bakim & N. Cicek
34. Investigating and comparing the two superstorms in the 23rd solar cycle F. Basciftci
35. Energy as a tracer of magnetospheric processes: GUMICS-4 global MHD results and observations compared T. Pulkkinen et al.
36. Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices J. Uwamahoro & J. Habarulema
37. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al.
38. Prediction of the Dst Index with Bagging Ensemble-learning Algorithm S. Xu et al.
39. 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.
40. Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al.
41. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al.
42. A neural network approach to the classification of electron and proton whistlers X. Miniere et al.
43. Forecasting of DST variations from polar cap indices using neural networks M. Stepanova et al.
44. Forecasting of ionospheric critical frequency using neural networks O. Altinay et al.
45. ANN-Based Assessment of the Influence of Natural and Anthropogenic Forcing on Surface Air Temperature Variability Over the Indian Subcontinent B. Padmavathi et al.
46. Prediction of variations from Polar Cap indices using time-delay neural network M. Stepanova et al.
47. Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps J. Tian et al.
48. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al.
49. Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems H. Lundstedt
50. Progress in space weather predictions and applications H. Lundstedt
51. An operational approach to forecast the Earth’s radiation belts dynamics G. Bernoux et al.
52. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks J. Wu & H. Lundstedt
53. Purposeful Prediction of Space Weather Phenomena of Simulated Emotional Learning A. Gholipour et al.
54. Learning based brain emotional intelligence as a new aspect for development of an alarm system T. Babaie et al.
55. Black box modeling of magnetospheric dynamics to forecast geomagnetic activity A. Gholipour et al.
56. RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics M. Liemohn et al.
57. <i>Dst</i> Index Prediction Method Based on LSTM Neural Network S. LI et al.
58. 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.
59. 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
60. Solar origin of geomagnetic storms and prediction of storms with the use of neural networks H. Lundstedt
61. Daily geomagnetic field prediction of INTERMAGNET observatories data using the multilayer perceptron neural network S. Ouadfeul et al.
62. Twenty‐four hour predictions of f0F2 using time delay neural networks P. Wintoft & L. Cander
63. Operational models for forecasting Dst S. Watanabe et al.
64. Forecasting ionospheric structure during the great geomagnetic storms L. Cander & S. Mihajlovic
65. Cosmic ray variability and geomagnetic activity: A statistical study K. Kudela & M. Storini
66. A neural network study of the mapping from solar magnetic fields to the daily average solar wind velocity P. Wintoft & H. Lundstedt
67. Neural network-based nonlinear prediction of magnetic storms D. Jankovičová et al.
68. Time series, neural networks and the future of the Sun A. Conway
69. Neural network prediction of AE data J. Takalo & J. Timonen
70. A cellular automata-based model of Earth's magnetosphere in relation withDstindex A. Banerjee et al.
71. On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index A. Banerjee et al.
72. Operational forecasts of the geomagnetic Dst index H. Lundstedt et al.
73. Comparison of neural network and support vector machine methods for Kp forecasting E. Ji et al.
74. Automatic detection of sudden commencements using neural networks A. Segarra & J. Curto
75. Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters M. Stepanova et al.
76. Cosmic Rays in Relation to Space Weather K. Kudela et al.
77. Prediction of Partial Ring Current Index Using LSTM Neural Network H. LI et al.
78. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt
79. Model Evaluation Guidelines for Geomagnetic Index Predictions M. Liemohn et al.
80. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik
81. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale
82. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning A. Gholipour et al.
83. A neural network prediction of solar cycle 23 A. Conway et al.
84. Operational Dst index prediction model based on combination of artificial neural network and empirical model W. Park et al.
85. Space weather modelling with intelligent hybrid systems: Predicting the solar wind velocity P. Wintoft & H. Lundstedt
86. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt
87. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al.
88. Neural network prediction of the AE index from the PC index J. Takalo & J. Timonen
89. Using artificial neural networks in the investigation of four moderate geomagnetic storms (mGSs) that occurred in 2015 F. Basciftci
90. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov