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.
Highlights
The concept of space weather has been defined by the US National Space Weather Program as “Conditions on the Sun and in the solar wind, magnetosphere, ionosphere and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems and can endanger human life or health” [1]
This is because many infrastructures could be affected by significant flares and the cost of building an accurate solar flare prediction system would be much cheaper than the cost of repairing damage caused by such a flare
We introduce a solar flare prediction system, summarised in the following subsection, working solely with Geostationary Operational Environmental Satellite (GOES) X-ray flux data that integrates three neural networks to deliver these predictions and provides an automated prediction of solar flares by utilising deep learning techniques
Summary
The concept of space weather has been defined by the US National Space Weather Program as “Conditions on the Sun and in the solar wind, magnetosphere, ionosphere and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems and can endanger human life or health” [1]. UFCORIN (Universal Forecast Constructor by Optimized Regression of Inputs) is open-source software available online which has been used to predict general time series and solar flares. This system uses HMI image data and GOSE X-ray data as input to predict X, M, and C solar flare class. We introduce a solar flare prediction system, summarised in the following subsection, working solely with GOES X-ray flux data that integrates three neural networks to deliver these predictions and provides an automated prediction of solar flares by utilising deep learning techniques. The format of GOES Data is challenging as it is represented as a time-series signal, which makes it challenging for machinelearning based prediction (Deep learning in particular)
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