Predicting Solar Flares with Remote Sensing and Machine Learning
- URL: http://arxiv.org/abs/2110.07658v1
- Date: Thu, 14 Oct 2021 18:28:28 GMT
- Title: Predicting Solar Flares with Remote Sensing and Machine Learning
- Authors: Erik Larsen
- Abstract summary: Destruction of critical systems would lead to food shortages and an inability to respond to emergencies.
A solution to this impending problem is proposed herein using satellites in solar orbit that continuously monitor the Sun.
A system of systems approach will allow enough warning for safety measures to be put into place mitigating the risk of disaster.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: High energy solar flares and coronal mass ejections have the potential to
destroy Earth's ground and satellite infrastructures, causing trillions of
dollars in damage and mass human suffering. Destruction of these critical
systems would disable power grids and satellites, crippling communications and
transportation. This would lead to food shortages and an inability to respond
to emergencies. A solution to this impending problem is proposed herein using
satellites in solar orbit that continuously monitor the Sun, use artificial
intelligence and machine learning to calculate the probability of massive solar
explosions from this sensed data, and then signal defense mechanisms that will
mitigate the threat. With modern technology there may be only safeguards that
can be implemented with enough warning, which is why the best algorithm must be
identified and continuously trained with existing and new data to maximize true
positive rates while minimizing false negatives. This paper conducts a survey
of current machine learning models using open source solar flare prediction
data. The rise of edge computing allows machine learning hardware to be placed
on the same satellites as the sensor arrays, saving critical time by not having
to transmit remote sensing data across the vast distances of space. A system of
systems approach will allow enough warning for safety measures to be put into
place mitigating the risk of disaster.
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