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Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data
Nagem, Tarek A.M. Ipson, Stanley S. Wang, Z. Al-Waisy, Alaa S.
Nagem, Tarek A.M.
Ipson, Stanley S.
Wang, Z.
Al-Waisy, Alaa S.
Publication Date
2018
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©The Authors 2018. This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
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openAccess
Accepted for publication
2018-01-11
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Abstract
Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.
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Published version
Citation
Nagem TAMH, Qahwaji R, Ipson SS et al (2018) Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data. International Journal of Advanced Computer Science and Applications (IJACSA). 9(1): 492-498.
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Article