Abstract
Quite recently, considerable attention has been paid to solar flare prediction because extreme solar eruptions could affect our daily life activities and on different technologies. Therefore, this paper presents a novel method of the development of improved second-generation of the Automated Solar Activity Prediction system (ASAP). The suggested algorithm improves the ASAP system by expanding a period of training vector and generating new machine learning rules to be more successful. Two neural networks are responsible for determining whether the sunspots group will release flare as well as determining if the flare is an M-class or X-class. Several measurement criteria are applied to determine the extent of system performance also all results are provided in this paper. Furthermore, the quadratic score (QR) is used as a metric criterion to compare between the prediction of the proposed algorithm with the Space Weather Prediction Center (SWPC) between 2012 and 2013. The results exhibit that the proposed algorithm outperforms the old ASAP system. Keywords: Solar flares, Machine Learning, Neural network, Space, Prediction, weather.
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References
Koskinen, H., et al.: Space weather effects catalogue. ESA Space Weather Study (ESWS) (2001)
Pick, M., Lathuillere, C., Lilensten, J.: ESA space weather programme feasibility studies. Alcatel-LPCE Consortium (2001)
Lenz, D.: Understanding and predicting space weather. Ind. Phys. 9(6), 18–21 (2004)
Zirin, H., Liggett, M.A.: Delta spots and great flares. Sol. Phys. 113(1–2), 267–283 (1987)
Shi, Z., Wang, J.: Delta-sunspots and X-class flares. Sol. Phys. 149(1), 105–118 (1994)
Raboonik, A., et al.: Prediction of solar flares using unique signatures of magnetic field images. Astrophys. J. 834(1), 11 (2016)
Falconer, D.A., et al.: MAG4 versus alternative techniques for forecasting active region flare productivity. Space Weather 12(5), 306–317 (2014)
Hong, S., et al.: The automatic solar synoptic analyzer and solar wind prediction. In: AGU Fall Meeting Abstracts (2014)
Colak, T., Qahwaji, R.: Automated McIntosh-based classification of sunspot groups using MDI images. Sol. Phys. 248(2), 277–296 (2008)
Zell, H.: How SDO Sees the Sun (2017)
Qahwaji, R., Colak, T.: Automatic short-term solar flare prediction using machine learning and sunspot associations. Sol. Phys. 241(1), 195–211 (2007)
Fukunaga, R.: Statistical Pattern Recognition. Academic Press, Cambridge (1990)
Balch, C.C.: Updated verification of the space weather prediction center’s solar energetic particle prediction model. Space Weather 6(1) (2008)
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Abed, A.K., Qahwaji, R. (2020). The Automated Solar Activity Prediction System (ASAP) Update Based on Optimization of a Machine Learning Approach. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_53
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DOI: https://doi.org/10.1007/978-3-030-52243-8_53
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