High solar activity predictions through an artificial neural network
| Autor: | J. C. Ortiz-Alemán, Carlos Couder-Castañeda, A. Solís-Santomé, J. J. Hernández-Gómez, Mauricio Gabriel Orozco-del-Castillo |
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| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Computer science business.industry General Physics and Astronomy Statistical and Nonlinear Physics Pattern recognition Machine learning computer.software_genre 01 natural sciences Computer Science Applications Human health Computational Theory and Mathematics Harmonics 0103 physical sciences Pattern recognition (psychology) Statistical analysis Artificial intelligence business 010303 astronomy & astrophysics computer Mathematical Physics 0105 earth and related environmental sciences |
| Zdroj: | International Journal of Modern Physics C. 28:1750075 |
| ISSN: | 1793-6586 0129-1831 |
| DOI: | 10.1142/s0129183117500759 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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