Attention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images-Reference-Cited by-同舟云学术

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Attention‐Based Machine Vision Models and Techniques for Solar Wind Speed Forecasting Using Solar EUV Images

Published:2022-03 Issue:3 Volume:20 Page:
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Brown Edward J. E.¹²³^ORCID,Svoboda Filip¹,Meredith Nigel P.²^ORCID,Lane Nicholas¹⁴,Horne Richard B.²^ORCID

Affiliation:

1. Department of Computer Science and Technology University of Cambridge Cambridge UK

2. Space Weather and Atmosphere Team British Antarctic Survey NERC Cambridge UK

3. BAS AI Lab British Antarctic Survey NERC Cambridge UK

4. Samsung AI Center Cambridge UK

Funder

Natural Environment Research Council

Engineering and Physical Sciences Research Council

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021SW002976

Reference43 articles.

1. Using Gradient Boosting Regression to Improve Ambient Solar Wind Model Predictions

2. Differences between CME-driven storms and CIR-driven storms

3. The causes of recurrent geomagnetic storms

4. Dynamic time lag regression: Predicting what & when;Chandorkar M.;International Conference on Learning Representations,2019

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3. Prediction of Solar Wind Speed Through Machine Learning From Extrapolated Solar Coronal Magnetic Field;Space Weather;2024-06

4. Heliophysics and space weather information architecture and innovative solutions: Current status and ways forward;Advances in Space Research;2024-05

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