Using Support Vector Machine (SVM) and ionospheric Total Electron Content (TEC) data for solar flare predictions

Saed Saad Asaly, Lee-Ad Gottlieb, Yuval Reuveni
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Predicting where and when space weather events such as solar flares and X-rays bursts are likely to occur in a specific area of interest constitutes a significant challenge in space weather research. Space weather scientists are, therefore, gradually exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of space weather events from past distribution patterns. As solar flares emit extreme ultraviolet and
more » ... radiation, which leads to ionization effect in different layers of the ionosphere, most recent works related to solar flare predictions using machine learning (ML) techniques, focused on X-ray time series predictions. Here, we suggest using support vector machine for classifying subdaily and diurnal total electron content (TEC) spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M, and X-class solar flare events. This is done as opposed to predicting TEC time series using ML techniques. The predictions are estimated up to three days before each tested class events, along with different skill scores such as precision, recall, Heidke skill score (HSS), accuracy, and true skill statistics. The results indicate that the suggested approach has the ability to predict solar flare events of X and M-class 24 h prior to their occurrence with 91% and 76% HSS skill scores, respectively, which improves over most recent related works. However, for the small-size C and B-class flares, the suggested approach does not succeed in producing similar promising results. Index Terms-Ionospheric total electron content, machine learning (ML), solar flare predictions, space weather, support vector machine (SVM). This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see Saed Asaly received the B.Sc. and M.Sc. degrees in computer science and mathematics from Ariel University, Ariel, israel, in 2014 and 2018, respectively, where he is currently working toward the Ph.D. degree in computer science. His research interests include applying and developing machine learning and deep learning techniques with remote sensing data, in order to predict natural hazards. metric space, with emphasis on problems in machine learning, computational geometry, and metric embeddings. Yuval Reuveni received the B.Sc., M.Sc., and Ph.D. degrees in geophysics, atmospheric and space sciences from Tel-Aviv University, Tel Aviv, Israel, in 2002Israel, in , 2005Israel, in , and 2011 He was with the Eastern R&D center as the Head of the Department of Geophysics and Space Sciences, in 2015, and the Physics Department, Ariel University, in 2017. His research interests include combining various data analysis techniques and remote sensing measurements from ground and space-based instruments, to study electromagnetic wave propagation, ionospheric physics, space weather, and space geodesy phenomena.
doi:10.1109/jstars.2020.3044470 fatcat:f552an4byfavzhdjluafbst2tu