Home   >   CSC-OpenAccess Library   >    Manuscript Information
A Study of The Relationship Among Parameters of M/X Solar Flares via Association Rules
Jéssica de Farias Pereira, Ana Estela Antunes da Silva, André Leon Sampaio Gradvohl, Guilherme Palermo Coelho, José Roberto Cecatto
Pages - 63 - 77     |    Revised - 31-10-2019     |    Published - 01-12-2019
Volume - 8   Issue - 4    |    Publication Date - December 2019  Table of Contents
Solar Flares, Data Mining, Association Rules.
This paper introduces a method to study the relation among parameters that can cause the origin of M/X solar flares. Solar flares, especially flares of types M and X, make the Earth’s atmosphere more ionized and have an effect on radio signals, which can cause disruptions in wireless communications. This situation points out to the need for better identification of the parameters involved in M/X solar flares. The method is based on four categorical parameters and their relations. Relations are demonstrated by association rules which were extracted by the APRIORI algorithm and the most promising rules were filtered by support and confidence metrics. Results of the most promising rules had been compared by application to different periods of the 23rd and the 24th solar cycles.
1 Zenodo 
2 refSeek 
3 DataCite Search 
4 Scribd 
5 SlideShare 
A. Ajabshirizadeh, N. M. Jouzdani, S. Abbassi. "Neural network prediction of solar cycle 24". Research in Astronomy and Astrophysics, vol. 11(4), p. 491-496, 2011.
A. Loskutov, I. A. Istomin, K. M. Kuzanyan, O. L. Kotlyarov. "Testing and forecasting the time series of the solar activity by singular spectrum analysis." arXiv preprint - ArXiv, nlin/0010027. [Online] Available: https://arxiv.org/abs/nlin/0010027 [Nov. 24, 2019].
C. Basu, M. Padmanaban, S. Guillon, L. Cauchon, M. DeMontigny, I. Kamwa. "Association rule mining to understand GMDs and their effects on power systems," in Proc. IEEE Power and Energy Society General Meeting, 2016, pp.1-6.
C. Guo B. Xue, Z. Lin. "Study on the Prediction Method of Characteristic Parameters of Solar X-ray Flares". Chinese Astronomy and Astrophysics. vol 37(3). pp. 255-265, Jul. 2013;
C. Yanmei, W. Huaning. "Correlation between solar flare productivity and photospheric vector magnetic fields". Advances in Space Research. vol. 42 (9), pp. 1475-1479, Nov. 2018.
F. Zuccarello. "Multi-spectral observations of flares." Astronomische Nachrichten, vol.337(10), pp.1070. Nov. 2016.
G. Meera, R. Munika, A. K. Shrivastava. "Various Solar Activity Parameters and their Interrelationship from Solar cycles 20 to 24". International Research Journal of Science and Engineering. vol. 5 (5), pp 59-69. May. 2017.
G. Meera, V. K. Mishra, A. P. Mishra. "Solar activity parameters and their interrelationship: Continuous decrease in flare activity from solar cycles 20 to 23". Journal of Geophysical Research, vol. 112. pp 1-10. May, 2007.
J. Han, M. Kamber, J. Pei. "Data Mining: Concepts and Techniques". 3rd. ed. Morgan Kaufmann, 2012.
K. D. Leka, G. Barnes. "Solar Flare Forecasting: Present Methods and Challenges" in Extreme Events in Geospace, 1st ed., vol. 1. N. Buzulukova, Ed. Elsevier, 2017. pp 65-98.
K. Fox. "Impacts of Strong Solar Flares. 2013". Internet: https://www.nasa.gov/mission_pages/sunearth/news/flare-impacts.html#.WAGF2uArLIV. [Sep. 1, 2019].
L. Shen, J. Dun, X. Zhang, Y. Jiang. " Analysis of the Major Parameters in Solar Active Regions Based on the PCA Method". Chinese Astronomy and Astrophysics. vol. 39. pp 212 - 224. Jul. 2015.
Liu, Chang, Deng, Na, Wang, Haimin, and Wang, Jason T. L. "Predicting Solar Flares Using SDO /HMI Vector Magnetic Data Products and the Random Forest Algorithm". United States: N. p., 2017.
M. Dierckxsens, K. Tziotziou, S. Dalla, I. Patsou, M. S. Marsh, N. B. Crosby, O. Malandraki, G. Tsiropoula. "Relationship between Solar Energetic Particles and Properties of Flares and CMEs: Statistical Analysis of Solar Cycle 23 Events". Solar Physics. vol. 290(3), pp.841-874, Mar. 2015.
P. Tan, M. Steinbach, V. Kumar, V. "Association Analysis: Basic Concepts and Algorithms," in Introduction to Data Mining. P. Tan, M. Steinbach, V. Kumar (eds). London:Pearson, 2005. pp. 328-414.
R. Agrawal, R. Srikant. "Fast algorithms for mining association rules in large databases," in Proc VLDB, 1994, pp 487-499.
R. Qahwaji, T. Colak. "Automated Prediction of Solar Flares Using Neural Networks and Sunspots Associations". Advances in Soft Computing. vol 39. pp. 316 - 324. Jun. 2007.
R. Qahwaji, T. Colak. "Automatic Prediction of Solar Flares using Machine Learning: Practical Study on the Halloween Storm," in Proc. RAST 2007. pp. 739-742.
S. Feng, L; Yu, Y. Yang. "The relationship between grouped solar flares and sunspot activity," in Proc. Bull. Astr. Soc. India, 2013, pp. 237-246.
S. Sello, "Time Series Forecasting: A Nonlinear Dynamics Approach. Termo-Fluid Dynamics Research Center." arXiv preprint - ArXiv, physics/9906035. [Online] Available: https://arxiv.org/abs/physics/9906035 [Nov. 24, 2019].
SGAS Solar and Geophysical Activity Summary. Internet: ftp://ftp.swpc.noaa.gov/pub/forecasts/SGAS/README. [Aug. 10, 2019].
Silveira C.R., Cecatto J.R., Santos M.T.P., Ribeiro M.X. (2018) Thematic Spatiotemporal Association Rules to Track the Evolving of Visual Features and Their Meaning in Satellite Image Time Series. In: Latifi S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 738. Springer, Cham.
Space Weather Prediction Center: NOAA / NWS Space Weather Prediction Center. 2015. Internet: http://www.swpc.noaa.gov. [Aug. 17, 2019].
SRS Solar Region Summary. Internet: ftp://ftp.swpc.noaa.gov/pub/forecasts/SRS/README. [Aug. 10, 2010].
SWPC. Data Access: SWPC Data Service. 2017. Internet: http://www.swpc.noaa.gov/content/data-access. [Sep. 10, 2019].
T. Colak, R. Qahwaji. "Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares". Space Weather. vol. 7, pp 1-12, Jun. 2009.
T. T. Nguyen, C. P. Willis, D. J. Paddon, H. S. Nguyen. "On Learning of Sunspot Classification," in Intelligent Information Processing and Web Mining, 1st ed., vol. 25. M. A. Klopotek, S. T. Wierzchon, K. Trojanowski (eds). Berlin, Heidelberg: Springer. 2004. pp 59- 68.
THE R FOUNDATION. The R Project for Statistical Computing. Internet: https://www.r- project.org. [Aug. 12, 2019].
X. Yang, G. Le, C. Zhang, Z. Yin, W. Zhao. "A Statistical Analysis of X1- and X2- or Higher- Class Flares during Solar Cycles 21~23". Chinese Astronomy and Astrophysics. vol. 38, pp. 92 -99. Jun. 2014.
Miss Jéssica de Farias Pereira
School of Technology, University of Campinas, Limeira, 13484-332, Brazil - Brazil
Dr. Ana Estela Antunes da Silva
School of Technology, University of Campinas, Limeira, 13484-332, Brazil - Brazil
Dr. André Leon Sampaio Gradvohl
School of Technology, University of Campinas, Limeira, 13484-332, Brazil - Brazil
Dr. Guilherme Palermo Coelho
School of Technology, University of Campinas, Limeira, 13484-332, Brazil - Brazil
Dr. José Roberto Cecatto
Astrophysics Division, National Institute of Space Research, São José dos Campos, 12227-010, Brazil - Brazil