Analysis and Predictive Modeling of Solar Coronal Holes Using Computer Vision and ARIMA-LSTM Networks

TL;DR

This paper combines computer vision for coronal hole detection with ARIMA-LSTM models to analyze and forecast solar coronal hole sizes, aiding space weather prediction.

Contribution

It introduces a hybrid ARIMA-LSTM approach for predicting coronal hole areas and employs computer vision for their detection from solar imagery.

Findings

Effective detection of coronal holes using computer vision.

Accurate short-term prediction of coronal hole sizes.

Insights into coronal hole behavior over a week.

Abstract

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.