26 citations as recorded by crossref.

1. The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq R. Tang et al. 10.3390/atmos11040316
2. A regional model for the prediction of M(3000)F2 over East Asia J. Wang et al. 10.1016/j.asr.2020.01.026
3. Comparison between observed ionospheric foF2 and IRI-2001 predictions over periods of severe geomagnetic activities at Grahamstown, South Africa A. Adewale et al. 10.1016/j.asr.2009.09.026
4. A comparison of neural network-based predictions of foF2 with the IRI-2012 model at conjugate points in Southeast Asia N. Wichaipanich et al. 10.1016/j.asr.2017.03.023
5. Predicting TEC in China based on the neural networks optimized by genetic algorithm R. Song et al. 10.1016/j.asr.2018.03.043
6. An Artificial Neural Network‐Based Ionospheric Model to Predict NmF2 and hmF2 Using Long‐Term Data Set of FORMOSAT‐3/COSMIC Radio Occultation Observations: Preliminary Results V. Sai Gowtam & S. Tulasi Ram 10.1002/2017JA024795
7. Preliminary investigation of real‐time mapping of foF2 in northern China based on oblique ionosonde data C. Zhou et al. 10.1002/jgra.50262
8. Modeling the variations of reflection coefficient of Earth’s lower ionosphere using very low frequency radio wave data by artificial neural network K. Ghanbari et al. 10.1016/j.asr.2016.04.027
9. Predicting foF2 in the China region using the neural networks improved by the genetic algorithm R. Wang et al. 10.1016/j.jastp.2012.09.010
10. On the global model for foF2 using neural networks E. Oyeyemi et al. 10.1029/2004RS003223
11. A new global version of M(3000)F2 prediction model based on artificial neural networks S. Oronsaye et al. 10.1016/j.asr.2013.11.023
12. Upgrading CCIR'sfoF2maps using available ionosondes and genetic algorithms E. Gularte et al. 10.1016/j.asr.2017.08.019
13. A New Artificial Neural Network‐Based Global Three‐Dimensional Ionospheric Model (ANNIM‐3D) Using Long‐Term Ionospheric Observations: Preliminary Results V. Gowtam et al. 10.1029/2019JA026540
14. Neural network-based prediction techniques for global modeling of M(3000)F2 ionospheric parameter E. Oyeyemi et al. 10.1016/j.asr.2006.09.038
15. Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations W. Li et al. 10.3390/rs12050866
16. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al. 10.1002/2016RS006192
17. Modeling M(3000)F2 based on extreme learning machine H. Bai et al. 10.1016/j.asr.2019.09.021
18. A Near-Real-Time Forecasting Model of High-Frequency Radiowave Propagation Factor Fusion Based on the ICEEMDAN Decomposition and Bi-LSTM Methods Y. Shi et al. 10.1109/TAP.2024.3413298
19. Modelling M(3000)F2 at an African Equatorial Location for Better IRI‐Model Prediction B. Adebesin et al. 10.1029/2021RS007311
20. Towards the development of a new global foF2 empirical model using neural networks E. Oyeyemi & A. Poole 10.1016/j.asr.2004.06.010
21. foF2 correlation studies with solar and geomagnetic indices for two equatorial stations E. Joshua & N. Nzekwe 10.1016/j.jastp.2012.02.015
22. Variation in the ionospheric propagating factor M(3000)F2 at Ouagadougou, Burkina Faso O. Oyekola 10.1016/j.asr.2010.02.017
23. A single‐station empirical model for TEC over the Antarctic Peninsula using GPS‐TEC data J. Feng et al. 10.1002/2016RS006171
24. Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network K. Reddybattula et al. 10.3390/universe8110562
25. 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
26. A new global model for the ionospheric F2 peak height for radio wave propagation M. Hoque & N. Jakowski 10.5194/angeo-30-797-2012