18 citations as recorded by crossref.

1. One day prediction of nighttime VLF amplitudes using nonlinear autoregression and neural network modeling H. Santosa & Y. Hobara https://doi.org/10.1002/2016RS006022
2. Forecasting of global data‐binning parameters for high‐resolution empirical geomagnetic field models M. Sitnov et al. https://doi.org/10.1029/2012SW000783
3. Evidence for the geomagnetic gradient induced current in the ionosphere around the South Atlantic Anomaly S. Yi et al. https://doi.org/10.1063/5.0244785
4. Development of a 3‐D Plasmapause Model With a Back‐Propagation Neural Network Z. Zheng et al. https://doi.org/10.1029/2019SW002360
5. Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning D. Conde et al. https://doi.org/10.1029/2023SW003474
6. Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image H. XIAO & X. TIAN https://doi.org/10.11728/cjss2023.03.2022-0033
7. Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis S. Kiakojoori & K. Khorasani https://doi.org/10.1007/s00521-015-1990-0
8. A forecast model of geomagnetic indices from the solar wind fluids observations based on long short-term memory neural network Z. Zou et al. https://doi.org/10.1063/5.0196284
9. Prediction of Ionospheric TEC Based on the NARX Neural Network L. Guoyan et al. https://doi.org/10.1155/2021/7188771
10. Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices J. Uwamahoro & J. Habarulema https://doi.org/10.1186/1880-5981-66-95
11. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients J. Liu et al. https://doi.org/10.1007/s11207-024-02340-9
12. Prediction of Solar Wind Speed at 1 AU Using an Artificial Neural Network Y. Yang et al. https://doi.org/10.1029/2018SW001955
13. The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach I. Popova et al. https://doi.org/10.3390/e20090691
14. A comparison of machine-learning techniques for the prediction of the auroral electrojet index S. Gopinath & P. Prince https://doi.org/10.1007/s12040-019-1194-6
15. Auroral ultraviolet images for prediction of the auroral electrojet index J. Li et al. https://doi.org/10.1007/s11600-025-01589-3
16. Composite Model for Predicting SYM‐H Index Y. Ji et al. https://doi.org/10.1029/2022EA002560
17. Evaluating the Geoeffectiveness of Interplanetary Coronal Mass Ejections: Insights from a Support Vector Machine Approach with SHAP Value Analysis Y. Ye et al. https://doi.org/10.3847/1538-4357/ad61d7
18. Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks F. Siciliano et al. https://doi.org/10.1029/2020SW002589