Abstract
This paper illustrates using Rough set theory as a data mining method for modeling Alert systems. A data-driven approach is applied to design a reliable alert system for prediction of different situations and setting off of the alerts for various critical parts of human industry sections. In this system preprocessing and reduction of data with data mining methods is performed. Rough set learning method is used to attain the regular and reduced knowledge from the system behaviors. Finally, using the produced and reduced rules extracted from rough set reduction algorithms, the obtained knowledge is applied to reach this purpose. This method, as demonstrated with successful realistic applications, makes the present approach effective in handling real world problems. Our experiments indicate that the proposed model can handle different groups of uncertainties and impreciseness accuracy and get a suitable predictive performance when we have several certain features set for representing the knowledge.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Mitra, S., Pal, S.K., Mitra, P.: Data mining in Soft Computing Framework: A Survey. IEEE transactions on neural networks 13(1) (January 2002)
Lundstedt, H.: Neural networks and prediction of solar terrestrial effects. Planet Space Science 40, 457–464 (1992)
Boberg, F., Wintoft, P., Lundstedt, H.: Real time Kp predictions from solar wind data using neural networks. Phys. Chem. Earth. 25(4), 275–280 (2000)
Gholipour, A., Abbaspour, A., Araabi, B.N., Lucas, C.: Enhancements in the prediction of solar activity by locally linear model tree. In: Proc. of MIC 2003: 22nd Int. Conf. on Modeling, Identification and Control, Innsbruck, Austria, pp. 158–161 (2003)
Gholipour, A., Lucas, C., Araabi, B.N., Mirmomeni, M., Shafiee, M.: Extracting the main patterns of natural time series for long-term neurofuzzy prediction. Neural Comput. & Applic. Springer, London (2006), doi:10.1007/s00521-006-0062-x
Attia, A.F., Hamed, R.A., Quassim, M.: Prediction of Solar Activity Based on Neuro-Fuzzy Modeling. Springer Solar Physics 227, 177–191 (2005)
Gholipour, A., Araabi, B.N., Lucas, C.: Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study. Springer Neural Processing Letters 24, 217–239 (2006)
Gholipour, A., Lucas, C., Araabi, B.N., Shafiee, M.: Solar activity forecast: Spectral analysis and neurofuzzy prediction. Elsevier Journal of Atmospheric and Solar-Terrestrial Physics 67, 595–603 (2005)
Prestes, A., Rigozo, N.R., Echera, E., Vieira, L.E.A.: Spectral analysis of sunspot number and geomagnetic indices (1868–2001). Journal of Atmospheric and Solar-Terrestrial Physics 68, 182–190 (2006)
Lucas, C., Abbaspour, A., Gholipour, A., Araabi, B.N., Fatourechi, M.: Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorithm. Informatica 27(2), 165–174 (2003)
Babaie, T., Karimizandi, R., Lucas, C.: Prediction of solar conditions by emotional learning. Intelligent Data Analysis 9, 1–15 (2006)
Chen, Y.P., Wu, S.N., Wang, J.S.: A Hybrid Predictor for Time Series Prediction. IEEE, Los Alamitos (2004), 0-7803–8359
Mahini, R., Lucas, C., Mirmomeni, M.: Designing a New Alert System Based on KM in Fuzzy Expert System. In: 2nd Int. Conf. knowledge management. KMCM (2008)
Cloete, I., Zurada, J.M.: Knowledge-Based Neurocomputing. MIT Press, Cambridge (2000)
Li, R.F., Wang, X.Z.: Dimension reduction of process dynamic trends using independent component analysis. Computers and chemical engineering 26, 467–473 (2002)
Chimphlee, S., Salim, N., Ngadiman, M.S.B., Chimphlee, W., Srinoy, S.: Independent component analysis and rough fuzzy based approach to web usage mining. In: Proceeding of 24th IASTED international Multi-Conference Artificial Intelligence and applications, February 13–16 (2006)
De Zeeuw, D.L., Gombosi, T.I., Groth, C.P., Powell, K.G., Stout, F.: An adaptive MHD method for global space weather simulations. IEEE Tran. on Plasma Science 28(6), 1956–1965 (2002)
Horton, W., Doxas, I.: A low dimensional dynamical model for the solar wind driven geotail-ionosphere system. Journal of Geophysical Research 103, 4561–4572 (1998)
Freeman, J., Nagai, A., Reiff, P., Denig, W., Gussenhoven, S.S., Heinermann, M., Rich, F., Hairston, M.: The use of neural networks to predict magnetospheric parameters for input to a magnetospheric forecast model. In: Joselyn, J., Lundstedt, H., Trollinger (eds.) Artificial Intelligence Applications in Solar Terrestrial Physics, Boulder, Colorado, vol. 167, Natl. Oceanic and Atmos. Admin, Boulder, Colorado (1994)
Gleisner, H., Lundstedt, H., Wintoft, P.: Predicting geomagnetic storms from solar wind data using time delay neural networks. Annales Geophysicae 14, 679–686 (1996)
Gleisner, H.: Solar wind and Geomagnetic activity: predictions using neural networks. PhD thesis, Lund University, Lund, Sweden (2000)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Ziarko, W. (ed.): Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceeding of the International Workshop on Rough Sets and Knowledge Discovery (RSKD 1993), Banff, Alberta, Canada, October 12–15. Springer, Heidelberg (1993)
Huynh, V., Ho, T., Nakamori, Y.: An Overview on the Approximation Quality Based on Rough-Fuzzy Hybrids. Studies in Fuzziness and Soft Computing (2008)
Shen, Q., Chouchoulas, A.: A rough-fuzzy approach for generating classification rules. Pattern Recognition Society, pp. 31–3203. Elsevier Science Ltd., Amsterdam (2002)
Jensen, R., Shen, Q.: Fuzzy–rough attribute reduction with application to web categorization. Fuzzy Sets and Systems 141, 469–485 (2004)
The Space Physics Interactive Data Resource, http://spidr.ngdc.noaa.gov/spidr/index.jsp
NOAA’s National Geophysical Data Center, http://www.nesdis.noaa.gov
Space Weather Alerts Archives, http://www.swpc.noaa.gov/alerts/archive
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mahini, R., Lucas, C., Mirmomeni, M., Rezazadeh, H. (2011). A Rough Set Approach Aim to Space Weather and Solar Storms Prediction. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21928-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-21928-3_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21927-6
Online ISBN: 978-3-642-21928-3
eBook Packages: Computer ScienceComputer Science (R0)