Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks
This work addresses space weather forecasting for satellite and aircraft operations, but it is incremental as it applies an existing deep learning method to a specific domain.
The study tackled predicting coronal hole areas to anticipate space weather effects by using LSTM networks on SDO imagery, achieving predictions over a seven-day span.
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.