70 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
2. Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al.
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.
4. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov
5. Exploring Three Recurrent Neural Network Architectures for Geomagnetic Predictions P. Wintoft & M. Wik
6. Neural network modeling of solar wind‐magnetosphere interaction J. Wu & H. Lundstedt
7. A neural network study of the mapping from solar magnetic fields to the daily average solar wind velocity P. Wintoft & H. Lundstedt
8. A neural network prediction of solar cycle 23 A. Conway et al.
9. Single particle simulation on the storm-time ring current formation and DST variation Y. Ebihara et al.
10. Precipitation prediction with neural networks L. Bodri
11. Nonlinear dynamical-information magnetosphere models for space weather forecasting O. Cheremnykh et al.
12. Purposeful Prediction of Space Weather Phenomena by Simulated Emotional Learning A. Gholipour et al.
13. Auroral electrojet predictions with dynamic neural networks H. Gleisner & H. Lundstedt
14. Prediction of the maximum observed frequency of the ionospheric HF radio channel using the method of artificial neural networks N. Barkhatov et al.
15. Systems theory for geospace plasma dynamics D. Vassiliadis
16. Solar origin of geomagnetic storms and prediction of storms with the use of neural networks H. Lundstedt
17. Behavior of compressed plasmas in magnetic fields G. Ganguli et al.
18. Prediction of the Dst Index with Bagging Ensemble-learning Algorithm S. Xu et al.
19. Zonal geomagnetic indices estimation of the two super geomagnetic activities of 2015 with the artificial neural networks E. Eroglu
20. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al.
21. Dst index estimation from cosmic ray and solar wind data during solar cycle 24 using artificial neural networks H. Sabeha et al.
22. Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia L. Bodri & V. Čermák
23. Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters M. Stepanova et al.
24. Purposeful Prediction of Space Weather Phenomena of Simulated Emotional Learning A. Gholipour et al.
25. Phase shift (time) between storm-time maximum negative excursions of geomagnetic disturbance index Dst and interplanetary Bz R. Kane & E. Echer
26. Optical finite impulse response neural networks P. Silveira et al.
27. 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.
28. Forecasting of DST variations from polar cap indices using neural networks M. Stepanova et al.
29. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions J. Sharifie et al.
30. Forecasting electron precipitation based on predicted geomagnetic activity M. Wüest et al.
31. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling J. Sharifi et al.
32. Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems H. Lundstedt
33. Black box modeling of magnetospheric dynamics to forecast geomagnetic activity A. Gholipour et al.
34. Learning based brain emotional intelligence as a new aspect for development of an alarm system T. Babaie et al.
35. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm S. Kugblenu et al.
36. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al.
37. Neural network-based nonlinear prediction of magnetic storms D. Jankovičová et al.
38. An hybrid neuro-wavelet approach for long-term prediction of solar wind C. Napoli et al.
39. Bézier cubics’ agreement with the neural network of the TEC map E. Eroglu
40. Short-term prediction of fof2 using time-delay neural network P. Wintoft & L. Cander
41. Prediction of variations from Polar Cap indices using time-delay neural network M. Stepanova et al.
42. Probabilistic Geomagnetic Storm Forecasting via Deep Learning A. Tasistro‐Hart et al.
43. Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results R. Henwood et al.
44. Reliable prediction intervals with regression neural networks H. Papadopoulos & H. Haralambous
45. Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach C. Napoli et al.
46. Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method Y. Li et al.
47. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu
48. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale
49. Prediction of daily average solar wind velocity from solar magnetic field observations using hybrid intelligent systems P. Wintoft & H. Lundstedt
50. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al.
51. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks J. Wu & H. Lundstedt
52. Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting V. Gavrishchaka & S. Ganguli
53. Twenty‐four hour predictions of f0F2 using time delay neural networks P. Wintoft & L. Cander
54. Control of the current state of geomagnetic activity based on the satellite magnetic measurements in the magnetosphere A. Levitin et al.
55. 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
56. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt
57. The Bézier curve and neural network model of the time-domain transient signals E. Eroglu & O. Tretyakov
58. Using artificial neural networks in the investigation of four moderate geomagnetic storms (mGSs) that occurred in 2015 F. Basciftci
59. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al.
60. Artificial neural network technique for predicting the critical frequency of the ionospheric F 2 layer N. Barkhatov et al.
61. Machine learning in orbit estimation: A survey F. Caldas & C. Soares
62. Comparison of the first four weak and moderate geomagnetic storms of the 2022 using artificial neural networks K. Koklu
63. Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events V. Gavrishchaka & S. Ganguli
64. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt
65. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al.
66. Examining the Super Intense Geomagnetic Storm on 10–11 May, 2024 via Artificial Neural Networks S. Bulbul et al.
67. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al.
68. Space weather modelling with intelligent hybrid systems: Predicting the solar wind velocity P. Wintoft & H. Lundstedt
69. The bimodal magnetosphere and ring current, radiation belt, and tail transducers R. Sheldon
70. Discussing Total Electron Content over the Solar Wind Parameters E. Eroglu & T. Muhammad