Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks

TL;DR

This paper employs deep learning, specifically LSTM networks, combined with computer vision to predict the size and behavior of solar coronal holes over a week, enhancing space weather forecasting capabilities.

Contribution

It introduces a novel approach integrating computer vision and LSTM models to forecast coronal hole areas from solar imagery, advancing space weather prediction methods.

Findings

LSTM models effectively predict coronal hole area trends.

Computer vision accurately identifies coronal hole regions.

Improved forecasting of space weather impacts.

Abstract

In the era of space exploration, the implications of space weather have become increasingly evident. Central to this is the phenomenon of coronal holes, which can significantly influence the functioning of satellites and aircraft. These coronal holes, present on the sun, are distinguished by their open magnetic field lines and comparatively cooler temperatures, leading to the emission of solar winds at heightened rates. To anticipate the effects of these coronal holes on Earth, our study harnesses computer vision to pinpoint the coronal hole regions and estimate their dimensions using imagery from the Solar Dynamics Observatory (SDO). Further, we deploy deep learning methodologies, specifically the Long Short-Term Memory (LSTM) approach, to analyze the trends in the data related to the area of the coronal holes and predict their dimensions across various solar regions over a span of seven days. By evaluating the time series data concerning the area of the coronal holes, our research seeks to uncover patterns in the behavior of coronal holes and comprehend their potential influence on space weather occurrences. This investigation marks a pivotal stride towards bolstering our capacity to anticipate and brace for space weather events that could have ramifications for Earth and its technological apparatuses.