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Advances in Meteorology
Volume 2014, Article ID 735491, 13 pages
http://dx.doi.org/10.1155/2014/735491
Research Article

Prediction of Tropical Cyclones’ Characteristic Factors on Hainan Island Using Data Mining Technology

1Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2China Electric Power Research Institute, Beijing 100192, China
3Hainan Power Grid Corporation, Haikou, Hainan 570203, China

Received 18 August 2014; Revised 20 October 2014; Accepted 28 October 2014; Published 20 November 2014

Academic Editor: Luis Gimeno

Copyright © 2014 Ruixu Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. National Climate Center of China, Typhoon “Damrey” Has Caused Serious Damages to Hainan Province, 2005, http://ncc.cma.gov.cn/Website/index.php?NewsID=1454.
  2. J. S. Pedro, F. Burstein, and A. Sharp, “A case-based fuzzy multicriteria decision support model for tropical cyclone forecasting,” European Journal of Operational Research, vol. 160, no. 2, pp. 308–324, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. X. Qin and M. Mu, “A Study on the reduction of forecast error variance by three adaptive observation approaches for tropical cyclone prediction,” Monthly Weather Review, vol. 139, no. 7, pp. 2218–2232, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Wang, W. Zhang, and W. Fu, “Back Propogation(BP)-neural network for tropical cyclone track forecast,” in Proceedings of the 19th International Conference on Geoinformatics, pp. 1–4, IEEE, Shanghai, China, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Feng and J. N. K. Liu, “An adaptive neural network classifier for tropical cyclone prediction using a two-layer feature selector,” in Advances in Neural Networks—ISNN 2005, vol. 3497 of Lecture Notes in Computer Science, pp. 399–404, Springer, Berlin, Germany, 2005. View at Google Scholar
  6. H.-J. Song, S.-H. Huh, J.-H. Kim, C.-H. Ho, and S.-K. Park, “Typhoon track prediction by a support vector machine using data reduction methods,” in Computational Intelligence and Security, vol. 3801 of Lecture Notes in Computer Science, pp. 503–511, Springer, Berlin, Germany, 2005. View at Google Scholar
  7. M. DeMaria, “A simplified dynamical system for tropical cyclone intensity prediction,” Monthly Weather Review, vol. 137, no. 1, pp. 68–82, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. H. R. Winterbottom, E. W. Uhlhorn, and E. P. Chassignet, “A design and an application of a regional coupled atmosphere-ocean model for tropical cyclone prediction,” Journal of Advances in Modeling Earth Systems, vol. 4, no. 10, Article ID M10002, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. L. M. Ma and Z. M. Tan, “Improving the behavior of the cumulus parameterization for tropical cyclone prediction: convection trigger,” Atmospheric Research, vol. 92, no. 2, pp. 190–211, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. S. Gall, I. Ginis, S.-J. Lin, T. P. Marchok, and J.-H. Chen, “Experimental tropical cyclone prediction using the GFDL 25-km-resolution global atmospheric model,” Weather and Forecasting, vol. 26, no. 6, pp. 1008–1019, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Lv, “The Northwestern Pacific typhoon track forecast in 2005,” in Proceedings of the Meteorological Science and Technology Symposium on Taiwan Strait, p. 7, Chinese Meteorological Society, 2006, (Chinese).
  12. D. Zhang, Y. Zhang, T. Hu, B. Xie, and J. Xu, “A comparison of HY-2 and QuikSCAT vector wind products for tropical cyclone track and intensity development monitoring,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1365–1369, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Ying, W. Zhang, H. Yu et al., “An overview of the China meteorological administration tropical cyclone database,” Journal of Atmospheric and Oceanic Technology, vol. 31, no. 2, pp. 287–301, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. G. E. Forsythe, “Generation and use of orthogonal polynomials for data-fitting with a digital computer,” Journal of the Society for Industrial & Applied Mathematics, vol. 5, no. 2, pp. 74–88, 1957. View at Google Scholar
  15. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, “Constrained k-means clustering with background knowledge,” in Proceedings of the 18th International Conference on Machine Learning, vol. 1, pp. 577–584, 2001.
  16. Y. Ye, “Neighborhood density method for selecting initial cluster centers in K-mean clustering,” in Proceedings of the Workshop on Data Mining for Biomedical Applications (PAKDD '06), pp. 189–198, 2006.
  17. P. Xiong, Data Mining Algorithm and Clementine Practice, Tsinghua University Press, Beijing, China, 2011 (Chinese).
  18. S. L. Crawford, “Extensions to the CART algorithm,” International Journal of Man-Machine Studies, vol. 31, no. 2, pp. 197–217, 1989. View at Publisher · View at Google Scholar · View at Scopus
  19. D. M. Allen, “Mean square error of prediction as a criterion for selecting variables,” Technometrics, vol. 13, pp. 469–475, 1971. View at Google Scholar
  20. S. Yu and J. Shen, “Forward and backward algorithms for selecting predictors on the basis of the criterion from prediction sum of squares and their application,” Acta Meteorologica Sinica, vol. 1, pp. 83–90, 1988. View at Google Scholar
  21. D. Yao and S. Yu, “The stepwise algorithm of selecting forecast factors based on PRESS rule,” Journal of Atmospheric Sciences, vol. 2, pp. 129–135, 1992 (Chinese). View at Google Scholar
  22. Y. Lu, Mathematical Statistics Methods, East China University of Science and Technology Press, Shanghai, China, 2005, (Chinese).
  23. K. Xie and B. Liu, “An ENSO-forecast independent statistical model for the prediction of annual Atlantic tropical cyclone frequency in April,” Advances in Meteorology, vol. 2014, Article ID 248148, 11 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Yu, J. Tang, Y. Dai, and B. Yu, “The error and cause analysis of China's typhoon path prediction,” Journal of Weather, vol. 6, pp. 695–700, 2012. View at Google Scholar
  25. S. Ma, A. Qu, and Z. Yu, “The parallelization of typhoon numerical prediction model of and track forecast error analysis,” Journal of Applied Meteorology, vol. 3, pp. 322–328, 2004 (Chinese). View at Google Scholar