17 citations as recorded by crossref.

1. Long-Term Forecasting of Solar Activity Indices Using Neural Networks N. Barkhatov et al. https://doi.org/10.1023/A:1013019328034
2. Artificial neural network technique for predicting the critical frequency of the ionospheric F 2 layer N. Barkhatov et al. https://doi.org/10.1007/s11141-005-0043-4
3. Modeling evapotranspiration prediction on nursery area using recurrent neural networks R. Tawegoum et al. https://doi.org/10.1016/S1474-6670(17)38695-0
4. Predicting the Functional Dependence of the Sunspot Number in the Solar Activity Cycle Based on Elman Artificial Neural Network I. Krasheninnikov & S. Chumakov https://doi.org/10.31857/S0016794022600612
5. Progress in space weather predictions and applications H. Lundstedt https://doi.org/10.1016/j.asr.2003.09.072
6. Forecasting of the critical frequency of the ionosphereF2 layer by the method of artificial neural networks N. Barkhatov https://doi.org/10.1029/2004GI000065
7. A novel model to deal with ambiguous and complex time series: Application to sunspots forecasting P. Singh https://doi.org/10.1016/j.knosys.2025.114257
8. Forecasting ionospheric structure during the great geomagnetic storms L. Cander & S. Mihajlovic https://doi.org/10.1029/97JA02418
9. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale https://doi.org/10.1029/2018SW002061
10. Forecasting Solar Cycle 25 with Physical Model-Validated Recurrent Neural Networks A. Espuña Fontcuberta et al. https://doi.org/10.1007/s11207-022-02104-3
11. Predicting the Functional Dependence of the Sunspot Number in the Solar Activity Cycle Based on Elman Artificial Neural Network I. Krasheninnikov & S. Chumakov https://doi.org/10.1134/S0016793222600904
12. Forecasting solar cycle 24 using neural networks J. Uwamahoro et al. https://doi.org/10.1016/j.jastp.2008.12.003
13. Recovery of missing information in graph sequences by means of reference pattern matching and decision tree learning H. Bunke et al. https://doi.org/10.1016/j.patcog.2005.10.011
14. Prediction of the maximum observed frequency of the ionospheric HF radio channel using the method of artificial neural networks N. Barkhatov et al. https://doi.org/10.1134/S0016793206010099
15. Prediction of Solar Wind Speed at 1 AU Using an Artificial Neural Network Y. Yang et al. https://doi.org/10.1029/2018SW001955
16. Forecasting the solar cycle 25 using a multistep Bayesian neural network I. Bizzarri et al. https://doi.org/10.1093/mnras/stac2013
17. Forecasting Solar Cycle 25 Using Deep Neural Networks B. Benson et al. https://doi.org/10.1007/s11207-020-01634-y