Analysis and Predictive Modeling of Solar Coronal Holes Using Computer Vision and ARIMA-LSTM Networks
This work addresses space weather forecasting for satellite and aircraft safety, but it appears incremental as it combines existing methods without major breakthroughs.
This study tackled the problem of predicting solar coronal hole areas to understand space weather impacts by using computer vision for detection and a hybrid ARIMA-LSTM model for forecasting over seven days, achieving predictions but without specific numerical results reported.
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.