24 citations as recorded by crossref.

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2. The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq R. Tang et al. 10.3390/atmos11040316
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4. 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
5. Upgrading CCIR'sfoF2maps using available ionosondes and genetic algorithms E. Gularte et al. 10.1016/j.asr.2017.08.019
6. 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
7. A regional model for the prediction of M(3000)F2 over East Asia J. Wang et al. 10.1016/j.asr.2020.01.026
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9. Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations W. Li et al. 10.3390/rs12050866
10. 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
11. 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
12. A neural network-based foF2 model for a single station in the polar cap R. Athieno et al. 10.1002/2016RS006192
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14. Predicting TEC in China based on the neural networks optimized by genetic algorithm R. Song et al. 10.1016/j.asr.2018.03.043
15. Modelling M(3000)F2 at an African Equatorial Location for Better IRI‐Model Prediction B. Adebesin et al. 10.1029/2021RS007311
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19. Variation in the ionospheric propagating factor M(3000)F2 at Ouagadougou, Burkina Faso O. Oyekola 10.1016/j.asr.2010.02.017
20. Preliminary investigation of real‐time mapping of foF2 in northern China based on oblique ionosonde data C. Zhou et al. 10.1002/jgra.50262
21. 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
22. A single‐station empirical model for TEC over the Antarctic Peninsula using GPS‐TEC data J. Feng et al. 10.1002/2016RS006171
23. 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
24. A new global model for the ionospheric F2 peak height for radio wave propagation M. Hoque & N. Jakowski 10.5194/angeo-30-797-2012