Articles | Volume 30, issue 6
https://doi.org/10.5194/angeo-30-963-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-30-963-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Regular paper
| 
12 Jun 2012
Regular paper |
| 12 Jun 2012
Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks
J. Uwamahoro
Department of Mathematics and Physics, Kigali Institute of Education [KIE], P.O. Box 5039 – Kigali, Rwanda
South African National Space Agency [SANSA], Space Science, 7200 Hermanus, South Africa
L. A. McKinnell
South African National Space Agency [SANSA], Space Science, 7200 Hermanus, South Africa
Department of Physics and Electronics, Rhodes University, Grahamstown 6140, South Africa
J. B. Habarulema
South African National Space Agency [SANSA], Space Science, 7200 Hermanus, South Africa
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- Predicting the Geoeffectiveness of CMEs Using Machine Learning A. Pricopi et al. https://doi.org/10.3847/1538-4357/ac7962
- 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
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- Dst index estimation from cosmic ray and solar wind data during solar cycle 24 using artificial neural networks H. Sabeha et al. https://doi.org/10.1016/j.jastp.2025.106462
- Intelligent prediction model for geoeffectiveness of coronal mass ejections X. Tang et al. https://doi.org/10.3389/fspas.2026.1839173
- Propagation of Solar Energetic Particles During Multiple Coronal Mass Ejection Events S. Pohjolainen et al. https://doi.org/10.1007/s11207-015-0835-4
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- Examining the Super Intense Geomagnetic Storm on 10–11 May, 2024 via Artificial Neural Networks S. Bulbul et al. https://doi.org/10.3390/atmos17030302
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- Two‐step forecast of geomagnetic storm using coronal mass ejection and solar wind condition R. Kim et al. https://doi.org/10.1002/2014SW001033
- Predicting the arrival time of CME associated with type-II radio burst using neural networks technique M. Nedal et al. https://doi.org/10.1007/s10509-019-3651-8
- 基于机器学习方法的短期太阳爆发活动预报模型综述 鑫. 黄 et al. https://doi.org/10.1360/N072023-0208
- Forbush Decrease Prediction Based on Remote Solar Observations M. Dumbović et al. https://doi.org/10.1007/s11207-015-0819-4
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- Validation of the CME Geomagnetic Forecast Alerts Under the COMESEP Alert System M. Dumbović et al. https://doi.org/10.1007/s11207-017-1120-5
- Relevance vector machines as a tool for forecasting geomagnetic storms during years 1996–2007 T. Andriyas & S. Andriyas https://doi.org/10.1016/j.jastp.2015.02.005
- Solar and interplanetary precursors of geomagnetic storms in solar cycle 23 J. Uwamahoro & L. McKinnell https://doi.org/10.1016/j.asr.2012.09.034
- A neural network system for estimation of the geomagnetic field disturbance O. Mandrikova et al. https://doi.org/10.1051/e3sconf/20161100013
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- Prediction of Solar Wind Speed at 1 AU Using an Artificial Neural Network Y. Yang et al. https://doi.org/10.1029/2018SW001955
- On the prediction of geoeffectiveness of CMEs during the ascending phase of SC24 using a logistic regression method D. Besliu-Ionescu et al. https://doi.org/10.1016/j.jastp.2019.04.017
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