68 citations as recorded by crossref.

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26. 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
27. Forecasting of DST variations from polar cap indices using neural networks M. Stepanova et al. 10.1016/j.asr.2003.09.073
28. Locally linear neurofuzzy modeling and prediction of geomagnetic disturbances based on solar wind conditions J. Sharifie et al. 10.1029/2005SW000209
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30. Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling J. Sharifi et al. 10.1186/BF03351929
31. Solar wind magnetosphere coupling: Predicted and modeled with intelligent hybrid systems H. Lundstedt 10.1016/S0079-1946(97)00187-0
32. Black box modeling of magnetospheric dynamics to forecast geomagnetic activity A. Gholipour et al. 10.1029/2003SW000039
33. 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
34. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm S. Kugblenu et al. 10.1186/BF03352234
35. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. 10.1029/2019SW002215
36. Neural network-based nonlinear prediction of magnetic storms D. Jankovičová et al. 10.1016/S1364-6826(02)00025-1
37. An hybrid neuro-wavelet approach for long-term prediction of solar wind C. Napoli et al. 10.1017/S174392131100679X
38. Bézier cubics’ agreement with the neural network of the TEC map E. Eroglu 10.1007/s00500-024-09863-0
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40. Prediction of variations from Polar Cap indices using time-delay neural network M. Stepanova et al. 10.1016/j.jastp.2005.02.027
41. Probabilistic Geomagnetic Storm Forecasting via Deep Learning A. Tasistro‐Hart et al. 10.1029/2020JA028228
42. Increasing Lifetime of Recurrent Sunspot Groups Within the Greenwich Photoheliographic Results R. Henwood et al. 10.1007/s11207-009-9419-5
43. Reliable prediction intervals with regression neural networks H. Papadopoulos & H. Haralambous 10.1016/j.neunet.2011.05.008
44. Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach C. Napoli et al. 10.1017/S1743921311006806
45. 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
46. Analysis of the first four moderate geomagnetic storms of the year 2015 E. Eroglu 10.1007/s12517-021-08816-3
47. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
48. Prediction of daily average solar wind velocity from solar magnetic field observations using hybrid intelligent systems P. Wintoft & H. Lundstedt 10.1016/S0079-1946(97)00186-9
49. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. 10.1029/2020SW002589
50. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks J. Wu & H. Lundstedt 10.1029/97JA00975
51. Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting V. Gavrishchaka & S. Ganguli 10.1029/2001JA900118
52. Twenty‐four hour predictions of f0F2 using time delay neural networks P. Wintoft & L. Cander 10.1029/1998RS002149
53. Control of the current state of geomagnetic activity based on the satellite magnetic measurements in the magnetosphere A. Levitin et al. 10.1134/S0016793210020039
54. 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
55. Response of the auroral electrojets to the solar wind modeled with neural networks H. Gleisner & H. Lundstedt 10.1029/96JA03068
56. The Bézier curve and neural network model of the time-domain transient signals E. Eroglu & O. Tretyakov 10.1016/j.cpc.2024.109211
57. 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
58. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al. 10.1007/s11207-024-02340-9
59. 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
60. Machine learning in orbit estimation: A survey F. Caldas & C. Soares 10.1016/j.actaastro.2024.03.072
61. Comparison of the first four weak and moderate geomagnetic storms of the 2022 using artificial neural networks K. Koklu 10.1016/j.asr.2023.11.029
62. Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events V. Gavrishchaka & S. Ganguli 10.1029/2000JA900137
63. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks J. Wu & H. Lundstedt 10.1029/96GL00259
64. Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks M. Cristoforetti et al. 10.1038/s41598-022-11721-8
65. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. 10.1002/2017SW001608
66. Space weather modelling with intelligent hybrid systems: Predicting the solar wind velocity P. Wintoft & H. Lundstedt 10.1016/S0273-1177(97)01100-9
67. The bimodal magnetosphere and ring current, radiation belt, and tail transducers R. Sheldon 10.1016/S0273-1177(99)00521-9
68. Discussing Total Electron Content over the Solar Wind Parameters E. Eroglu & T. Muhammad 10.1155/2022/9592008