35 citations as recorded by crossref.

1. Electron flux models for different energies at geostationary orbit R. Boynton et al. https://doi.org/10.1002/2016SW001506
2. Specifying High‐Altitude Electrons Using Low‐Altitude LEO Systems: The SHELLS Model S. Claudepierre & T. O'Brien https://doi.org/10.1029/2019SW002402
3. Radial Response of Outer Radiation Belt Relativistic Electrons During Enhancement Events at Geostationary Orbit V. Pinto et al. https://doi.org/10.1029/2019JA027660
4. Complex Systems Methods Characterizing Nonlinear Processes in the Near-Earth Electromagnetic Environment: Recent Advances and Open Challenges G. Balasis et al. https://doi.org/10.1007/s11214-023-00979-7
5. Relativistic Electron Flux Prediction at Geosynchronous Orbit Based on the Neural Network and the Quantile Regression Method H. Zhang et al. https://doi.org/10.1029/2020SW002445
6. Challenges and Opportunities in Magnetospheric Space Weather Prediction S. Morley https://doi.org/10.1029/2018SW002108
7. Quantitative Prediction of High‐Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning L. Wei et al. https://doi.org/10.1029/2018SW001829
8. Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters J. Wang et al. https://doi.org/10.1016/j.asr.2022.10.013
9. A Dynamical Model of Equatorial Magnetosonic Waves in the Inner Magnetosphere: A Machine Learning Approach R. Boynton et al. https://doi.org/10.1029/2020JA028439
10. NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux D. Landis et al. https://doi.org/10.1029/2021SW002774
11. Modeling and prediction of electron fluxes in GEO with NARMAX approach using data set with missing data points P. Li et al. https://doi.org/10.1016/j.asr.2026.05.093
12. System Identification of Local Time Electron Fluencies at Geostationary Orbit R. Boynton et al. https://doi.org/10.1029/2020JA028262
13. Research Progress in High-Energy Electron Flux Prediction Methods in Geosynchronous Orbit . Ke Han et al. https://doi.org/10.1134/S0038094624700357
14. The Influence of Solar Wind and Geomagnetic Indices on Lower Band Chorus Emissions in the Inner Magnetosphere R. Boynton et al. https://doi.org/10.1029/2018JA025704
15. Interplanetary Parameters Leading to Relativistic Electron Enhancement and Persistent Depletion Events at Geosynchronous Orbit and Potential for Prediction V. Pinto et al. https://doi.org/10.1002/2017JA024902
16. Global prediction of sub-relativistic and relativistic electron fluxes in the geosynchronous orbit using artificial neural networks Z. Zou et al. https://doi.org/10.1063/5.0245593
17. Geostationary electron dynamics: ICARE_NG2 observations and new analytical model of daily electron fluxes driven by solar wind conditions P. Caron et al. https://doi.org/10.1051/swsc/2025017
18. Artificial neural network prediction model for geosynchronous electron fluxes: Dependence on satellite position and particle energy D. Shin et al. https://doi.org/10.1002/2015SW001359
19. Prediction Models of ≥2 MeV Electron Daily Fluences for 3 Days at GEO Orbit Using a Long Short-Term Memory Network X. Sun et al. https://doi.org/10.3390/rs15102538
20. Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach X. Chu et al. https://doi.org/10.1029/2021SW002808
21. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. https://doi.org/10.1007/s11214-017-0412-2
22. Space‐Based Sentinels for Measurement of Infrared Cooling in the Thermosphere for Space Weather Nowcasting and Forecasting M. Mlynczak et al. https://doi.org/10.1002/2017SW001757
23. Modeling the Relationship of ≥2 MeV Electron Fluxes at Different Longitudes in Geostationary Orbit by the Machine Learning Method X. Sun et al. https://doi.org/10.3390/rs13173347
24. The System Science Development of Local Time‐Dependent 40‐keV Electron Flux Models for Geostationary Orbit R. Boynton et al. https://doi.org/10.1029/2018SW002128
25. Performance evaluation of SNB3GEO electrons flux forecasting model using LANL and GOES-13 observations B. Geletaw et al. https://doi.org/10.1016/j.asr.2022.11.044
26. Real‐Time SWMF at CCMC: Assessing the Dst Output From Continuous Operational Simulations M. Liemohn et al. https://doi.org/10.1029/2018SW001953
27. Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices C. Forsyth et al. https://doi.org/10.1029/2019SW002416
28. Using ARMAX Models to Determine the Drivers of 40–150 keV GOES Electron Fluxes L. Simms et al. https://doi.org/10.1029/2022JA030538
29. Forecast of the Energetic Electron Environment of the Radiation Belts S. Walker et al. https://doi.org/10.1029/2022SW003124
30. Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models L. Simms et al. https://doi.org/10.1029/2022SW003263
31. Classifier Neural Network Models Predict Relativistic Electron Events at Geosynchronous Orbit Better than Multiple Regression or ARMAX Models L. Simms & M. Engebretson https://doi.org/10.1029/2019JA027357
32. Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations V. Kalegaev et al. https://doi.org/10.1016/j.asr.2022.08.033
33. Electron Flux Dropouts at L ∼ 4.2 From Global Positioning System Satellites: Occurrences, Magnitudes, and Main Driving Factors R. Boynton et al. https://doi.org/10.1002/2017JA024523
34. Predicting >2 MeV electron fluxes in geosynchronous orbit using multiple satellite measurements Z. Zou et al. https://doi.org/10.1063/5.0315198
35. A modeling study of ≥2 MeV electron fluxes in GEO at different prediction time scales based on LSTM and transformer networks X. Sun et al. https://doi.org/10.1051/swsc/2024021