SH022
Prediction of Solar Transient Events using Machine Learning
Prediction of Solar Transient Events using Machine Learning
Session ID#: 159501
Session Description:
Reliable prediction of solar transient events (flares, coronal mass ejections, energetic particle events, etc.) is an important problem from both research and operational perspectives. Over the previous decade, there has been an exponentially-increasing number of attempts to apply machine learning-driven (ML-driven) approaches to forecast such events, induced by the growing amounts of high-quality observational data and accessibility of ML techniques. This session aims to highlight the advances in ML-driven attempts for solar transient event forecasting, the challenges associated with them, and the fundamental physical knowledge derived by them. We welcome all contributions that focus on but are not limited to 1) application of novel ML algorithms and approaches, 2) data preparation and augmentation, 3) forecast assessment/evaluation, 4) class-imbalance treatment strategies, 5) forecast interpretation, and 6) forecast operationalization.
Index Terms:
7513 Coronal mass ejections [SOLAR PHYSICS, ASTROPHYSICS, AND ASTRONOMY]
7514 Energetic particles [SOLAR PHYSICS, ASTROPHYSICS, AND ASTRONOMY]
7519 Flares [SOLAR PHYSICS, ASTROPHYSICS, AND ASTRONOMY]
7924 Forecasting [SPACE WEATHER]
Primary Convener: Viacheslav M Sadykov, Georgia State University, Atlanta, GA, United States
Conveners: Dustin Kempton1, Sumanth Rotti2 and Azim Ahmadzadeh1, (1)Georgia State University, Atlanta, GA, United States(2)Georgia State University, Physics and Astronomy, Atlanta, GA, United States
Student/Early Career Convener: Sumanth Rotti, Georgia State University, Physics and Astronomy, Atlanta, GA, United States
See more of: SPA-Solar and Heliospheric Physics
