2024/05/16 by Ju‐Young Yun, Yun, Juyoung, Jungmin Shin +1
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Solar Radiation and Photovoltaics #Solar and Space Plasma Dynamics #Solar and Stellar Astrophysics (astro-ph.SR)
paper · pdf · doi:10.48550/arxiv.2405.09802
openalex publication_date 2024/05/16 · openalex created_date 2024/05/20 · openalex updated_date 2026/07/28
In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize hybrid time series prediction model, specifically combination of Long Short-Term Memory (LSTM) networks and ARIMA, to analyze trends in the area of coronal holes and predict their areas across various solar regions over a span of seven days. By examining time series data, we aim to identify patterns in coronal hole behavior and understand their potential effects on space weather.