High solar activity predictions through an artificial neural network

M. G. Orozco-Del-Castillo (), J. C. Ortiz-Alemán (), C. Couder-Castañeda (), J. J. Hernández-Gómez and A. Solís-Santomé ()
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M. G. Orozco-Del-Castillo: Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas Norte 152, Col. San Bartolo Atepehuacan, Ciudad de México 07730, México
J. C. Ortiz-Alemán: Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas Norte 152, Col. San Bartolo Atepehuacan, Ciudad de México 07730, México
C. Couder-Castañeda: Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Belisario Domínguez 22, Col. Centro, Delegación Cuauhtémoc, Ciudad de México 06010, México
J. J. Hernández-Gómez: Centro de Desarrollo Aeroespacial, Instituto Politécnico Nacional, Belisario Domínguez 22, Col. Centro, Delegación Cuauhtémoc, Ciudad de México 06010, México
A. Solís-Santomé: Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Delegación Gustavo A. Madero, Ciudad de Mexico 07738, Mexico

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 06, 1-16

Abstract: The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24–26, which we found to occur before 2040.

Keywords: Artificial neural network (ANN); solar cycle prediction; pattern recognition; ground level enhancement (GLE); high solar activity (HSA) (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500759

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