30 citations as recorded by crossref.

1. A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals I. Srivani et al. https://doi.org/10.1109/LGRS.2019.2895112
2. Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak J. Dabbakuti et al. https://doi.org/10.1049/iet-rsn.2019.0394
3. TEC prediction with neural network for equatorial latitude station in Thailand K. Watthanasangmechai et al. https://doi.org/10.5047/eps.2011.05.025
4. Local TEC modelling and forecasting using neural networks A. Tebabal et al. https://doi.org/10.1016/j.jastp.2018.03.004
5. Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations J. Uwamahoro et al. https://doi.org/10.1016/j.asr.2019.01.027
6. Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN) S. Inyurt & A. Sekertekin https://doi.org/10.1007/s10509-019-3545-9
7. Extreme learning machine-based spherical harmonic for fast ionospheric delay modeling T. Zhao et al. https://doi.org/10.1016/j.jastp.2021.105590
8. An Ionospheric Total Electron Content Model with a Storm Option over Japan Based on a Multi-Layer Perceptron Neural Network W. Li & X. Wu https://doi.org/10.3390/atmos14040634
9. Predicting foF2 in the China region using the neural networks improved by the genetic algorithm R. Wang et al. https://doi.org/10.1016/j.jastp.2012.09.010
10. Enhanced neural network model for regional ionospheric modeling and evaluation under different solar-geomagnetic conditions Y. Dong et al. https://doi.org/10.1088/1361-6501/aca693
11. Modified solar flux index for upper atmospheric applications T. Maruyama https://doi.org/10.1029/2010JA016322
12. Preliminary investigation of real‐time mapping of foF2 in northern China based on oblique ionosonde data C. Zhou et al. https://doi.org/10.1002/jgra.50262
13. Empirical model of TEC response to geomagnetic and solar forcing over Balkan Peninsula P. Mukhtarov et al. https://doi.org/10.1016/j.jastp.2017.11.010
14. A Review of Machine Learning‐Based Ionospheric Spatial and Temporal Modeling S. Mao et al. https://doi.org/10.1029/2024JH000555
15. First results from the ionospheric tomography experiment using beacon TEC data obtained by means of a network along a longitude of 136°E over Japan S. Thampi & M. Yamamoto https://doi.org/10.5047/eps.2009.10.003
16. Improvement of international reference ionospheric model total electron content maps: a case study using artificial neural network in Egypt B. Mohamed et al. https://doi.org/10.1515/jag-2023-0002
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19. Regional ionospheric modeling using wavelet network model M. El-Diasty https://doi.org/10.1186/s41445-017-0007-y
20. Regional TEC modelling over Africa using deep structured supervised neural network M. Moses et al. https://doi.org/10.1016/j.geog.2020.05.004
21. Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method I. Mallika et al. https://doi.org/10.1109/JSTARS.2018.2877445
22. Performance analysis of Neural Networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations L. Mallika I et al. https://doi.org/10.1007/s10509-020-03821-6
23. Grid-Search-Optimized, Gated Recurrent Unit-Based Prediction Model for Ionospheric Total Electron Content S. Zhou et al. https://doi.org/10.3390/technologies13080347
24. Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach F. Song & S. Shi https://doi.org/10.3390/atmos15030351
25. Comparison of the Kriging and neural network methods for modeling foF2 maps over North China region C. Jiang et al. https://doi.org/10.1016/j.asr.2015.03.042
26. Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region J. Uwamahoro et al. https://doi.org/10.1029/2017RS006499
27. Reliable prediction intervals with regression neural networks H. Papadopoulos & H. Haralambous https://doi.org/10.1016/j.neunet.2011.05.008
28. Prediction of geodetic point velocity using MLPNN, GRNN, and RBFNN models: a comparative study B. Konakoglu https://doi.org/10.1007/s40328-021-00336-6
29. Machine learning based storm time modeling of ionospheric vertical total electron content over Ethiopia A. Nigusie et al. https://doi.org/10.1038/s41598-024-69738-0
30. Simultaneous evaluation of solar activity proxies during geomagnetic storms using principal component analysis: Case study of the African low and mid-latitude regions J. Uwamahoro et al. https://doi.org/10.1016/j.jastp.2025.106477