About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 397473, 12 pages
http://dx.doi.org/10.1155/2012/397473
Research Article

Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

1Faculty of Science and Technology, University of Macau, Taipa, Macau
2Laboratory & Research Center, Macao Water Supply Co. Ltd., Conselheiro Borja, Macau

Received 26 August 2012; Accepted 11 November 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Zhengchao Xie 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. Z. Selman, S. Greenhalgh, and R. Diaz, Eutrophication and Hypoxia in Coastal Areas: A Global Assessment of the State of Knowledge, World Resources Institute, Washington, DC, USA, 2008. View at Zentralblatt MATH
  2. J. Pallant, I. Chorus, and J. Bartram, “Toxic cyanobacteria in water,” in SPSS Survival Manual, 2007.
  3. R. Hecht-Nielsen, “Kolmogorov’s mapping neural network existence theorem,” in Proceedings of the 1st IEEE Internetional Jopint Conference of Neural Networks, New York, NY, USA, 1987.
  4. L. L. Rogers and F. U. Dowla, “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling,” Water Resources Research, vol. 30, no. 2, p. 457, 1994. View at Publisher · View at Google Scholar
  5. APHA, Standard Methods for the Examination of Water and Wastewater, American Public Health Association (APHA), American Water Works Association (AWWA) & Water Environment Federation (WEF), 2002.
  6. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  7. T. A. Stolarski, “A system for wear prediction in lubricated sliding contacts,” Lubrication Science, vol. 8, no. 4, pp. 315–351, 1996. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Li, Automotive engine tuning using least-square support vector machines and evolutionary optimization [Ph.D. thesis], University of Macau, 2011.
  9. Z. Liu, X. Wang, L. Cui, X. Lian, and J. Xu, “Research on water bloom prediction based on least squares support vector machine,” in Proceedings of the WRI World Congress on Computer Science and Information Engineering (CSIE '09), pp. 764–768, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. A. J. Smola and B. Scholkopf, 2003, http://alex.smola.org/papers/2003/SmoSch03b.pdf.
  11. H. Wang and D. Hu, “Comparison of SVM and LS-SVM for regression,” in Proceedings of the International Conference on Neural Networks and Brain Proceedings (ICNNB '05), pp. 279–283, October 2005. View at Scopus
  12. C. W. Hsu and C. C. Chang, A Practical Guide to Support Vector Classification, 2003.
  13. U. Çaydaş and S. Ekici, “Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel,” Journal of Intelligent Manufacturing, vol. 23, pp. 639–650, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Avci, “A new expert system for diagnosis of lung cancer: GDA-LS_SVM,” Journal of Medical Systems, vol. 36, pp. 2005–2009, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Çomak and A. Arslan, “A biomedical decision support system using LS-SVM classifier with an efficient and new parameter regularization procedure for diagnosis of heart valve diseases,” Journal of Medical Systems, vol. 36, pp. 549–556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Xu, X. Chen, and Q. Li, “INS/WSN-integrated navigation utilizing LS-SVM and H filtering,” Mathematical Problems in Engineering, vol. 2012, Article ID 707326, 19 pages, 2012. View at Publisher · View at Google Scholar
  17. C. Cattani, S. Chen, and G. Aldashev, “Information and modeling in complexity,” Mathematical Problems in Engineering, vol. 2012, Article ID 868413, 3 pages, 2012. View at Publisher · View at Google Scholar
  18. S. Chen, Y. Zheng, C. Cattani, and W. Wang, “Modeling of biological intelligence for SCM system optimization,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 769702, 10 pages, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  19. P. Lu, S. Chen, and Y. Zheng, “Artificial intelligence in civil engineering,” Mathematical Problems in Engineering. In press.