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Abstract and Applied Analysis
Volume 2014, Article ID 698632, 7 pages
http://dx.doi.org/10.1155/2014/698632
Research Article

The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area

1College of Communication Engineering, Chongqing University, Chongqing 400044, China
2School of Automation, Chongqing University, Chongqing 400044, China

Received 20 April 2014; Revised 27 June 2014; Accepted 27 June 2014; Published 5 August 2014

Academic Editor: Shen Yin

Copyright © 2014 Yuanchang Zhong 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. X.-B. Li, X.-A. Liu, and D.-L. Fu, “Analysis on water quality variation of the mainstream and tributaries in Wushan section, three Gorges Reservoir Region,” Environmental Science and Management, vol. 35, no. 5, pp. 122–128, 2010. View at Google Scholar
  2. Y. Xu, M. Zhang, L. Wang, L. Kong, and Q. Cai, “Changes in water types under the regulated mode of water level in Three Gorges Reservoir, China,” Quaternary International, vol. 244, no. 2, pp. 272–279, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Zhong and Y. Song, “Energy-saving adaptive routing algorithm for large-scale wireless sensor network,” Computer Engineering and Applications, vol. 49, no. 1, pp. 89–93, 2013. View at Google Scholar
  4. Y. Zhong, L. Cheng, L. Zhang, Y. Song, and H. R. Karimi, “Energy-efficient routing control algorithm in large-scale WSN for water environment monitoring with application to three gorges reservoir area,” The Scientific World Journal, vol. 2014, Article ID 802915, 9 pages, 2014. View at Publisher · View at Google Scholar
  5. S. Yin, X. Li, H. Gao, and O. Kaynak, “Data-based techniques focused on modern industry: an overview,” IEEE Transactions on Industrial Electronics, 2014. View at Publisher · View at Google Scholar
  6. S. Yin, G. Wang, and X. Yang, “Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data,” International Journal of Systems Science, vol. 45, no. 7, pp. 1375–1382, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  7. S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Transactions on Industrial Electronics, no. 99, 10 pages, 2014. View at Publisher · View at Google Scholar
  8. S. Yin, X. Gao, H. R. Karimi, and X. Zhu, “Study on support vector machine-based fault detection in tennessee eastman process,” Abstract and Applied Analysis, vol. 2014, Article ID 836895, 8 pages, 2014. View at Publisher · View at Google Scholar
  9. S. Yin, X. Zhu, and H. R. Karimi, “Quality evaluation based on multivariate statistical methods,” Mathematical Problems in Engineering, vol. 2013, Article ID 639652, 10 pages, 2013. View at Publisher · View at Google Scholar
  10. GB3838-2002, the surface water environment quality standard.
  11. K. Kambatla, G. Kollias, V. Kumar, and A. Grama, “Trends in big data analytics,” Journal of Parallel and Distributed Computing, vol. 74, no. 7, pp. 2561–2573, 2014. View at Publisher · View at Google Scholar
  12. B. K. Tannahill and M. Jamshidi, “System of systems and big data analytics-bridging the gap,” Computers & Electrical Engineering, vol. 40, no. 1, pp. 2–15, 2014. View at Google Scholar
  13. C. A. Steed, D. M. Ricciuto, G. Shipman et al., “Big data visual analytics for exploratory earth system simulation analysis,” Computers and Geosciences, vol. 61, pp. 71–82, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J.-P. Belaud, S. Negny, F. Dupros, D. Michéa, and B. Vautrin, “Collaborative simulation and scientific big data analysis: illustration for sustainability in natural hazards management and chemical process engineering,” Computers in Industry, vol. 65, no. 3, pp. 521–535, 2014. View at Google Scholar
  15. J. Fuzheng, “The general picture of the Three Gorges region,” Chongqing Architecture, vol. 9, no. 1, pp. 1–7, 2010. View at Google Scholar
  16. M. Samhouri, M. Abu-Ghoush, E. Yaseen, and T. Herald, “Fuzzy clustering-based modeling of surface interactions and emulsions of selected whey protein concentrate combined to l-carrageenan and gum Arabic solutions,” Journal of Food Engineering, vol. 91, no. 1, pp. 10–17, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. M. A. Ghoush, M. Samhouri, M. Al-Holy, and T. Herald, “Formulation and fuzzy modeling of emulsion stability and viscosity of a gum-protein emulsifier in a model mayonnaise system,” Journal of Food Engineering, vol. 84, no. 2, pp. 348–357, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. R. J. Hathaway and J. C. Bezdek, “Extending fuzzy and probabilistic clustering to very large data sets,” Computational Statistics and Data Analysis, vol. 51, no. 1, pp. 215–234, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  19. M. B. Al-Zoubi, A. Hudaib, and B. Al-Shboul, “A proposed fast Fuzzy C-Means algorithm,” WSEAS Transactions on Systems, vol. 6, no. 6, pp. 1191–1195, 2007. View at Google Scholar · View at Scopus
  20. S. R. Kannan, R. Devi, S. Ramathilagam, and A. Sathya, “Some robust objectives of FCM for data analyzing,” Applied Mathematical Modelling, vol. 35, no. 5, pp. 2571–2583, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. X. Weixin and L. Jianzhuang, “The mergence of hard clustering and fuzzy clustering-a fast FCM algorithm with two layers,” Fuzzy Systems and Mathematics, vol. 6, no. 2, pp. 77–85, 1992. View at Google Scholar
  22. X. Gao and W. Xie, “Advances in theory and applications of fuzzy clustering,” Chinese Science Bulletin, vol. 45, no. 11, pp. 961–970, 2000. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Tang, T. Fang, P. Du, and P. Shi, “Intra-dimensional feature diagnosticity in the Fuzzy Feature Contrast Model,” Image and Vision Computing, vol. 26, no. 6, pp. 751–760, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Samhouri, M. Abughoush, and T. Herald, “Fuzzy identification and modeling of a gum-protein emulsifier in a model mayonnaise color development system,” International Journal of Food Engineering, vol. 3, no. 4, article 11, 2007. View at Google Scholar · View at Scopus
  25. L. Wang, W. Wang, and Y.-X. Li, “Fuzzy clustering algorithm based on artificial immune cell mode,” Computer Engineering, vol. 37, no. 5, pp. 13–15, 2011. View at Google Scholar
  26. Z.-Y. Yang, X.-Y. Huang, C. H. Du, and M.-X. Tang, “Study of urban traffic congestion judgment based on FFCM clustering,” Application Research of Computers, vol. 25, no. 9, pp. 2768–2770, 2008. View at Google Scholar
  27. “Chongqing environmental monitoring center environment quality automatic monitoring of water quality weekly report[EB/OL],” 2014, http://www.cqemc.cn/.