coronal hole area
Analysis and Predictive Modeling of Solar Coronal Holes Using Computer Vision and LSTM Networks
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 deep learning methods, specifically Long Short-Term Memory (LSTM) networks, 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. This research enhances our ability to anticipate and prepare for space weather events that could affect Earth's technological systems.
- North America > United States > New York > Suffolk County > Stony Brook (0.04)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- Europe > United Kingdom > England > Greater Manchester > Manchester (0.04)
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- Government > Regional Government > North America Government > United States Government (0.33)
- Government > Space Agency (0.31)
Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks
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.
- North America > United States > New York > Suffolk County > Stony Brook (0.04)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- North America > United States > Colorado > Boulder County > Boulder (0.04)
- (5 more...)