• DocumentCode
    477475
  • Title

    Applying Artificial Neural Network Model in Assessing East Dongting Lake Wetland´s Ecosystem Carrying Capacity

  • Author

    Shi, Y.Z. ; Xin, D.J.

  • Author_Institution
    Sch. of Water Conservancy, Changsha Univ. of Sci. & Technol., Changsha
  • Volume
    1
  • fYear
    2008
  • fDate
    20-22 Oct. 2008
  • Firstpage
    179
  • Lastpage
    182
  • Abstract
    Because of the superiority in approximation, classification and study speed, the radial basis function artificial neural network (RBF-ANN) model is receiving more and more scholarspsila attention. Its framework, design, simulation and output of graphs are presented. With the aid of MATLAB tools, integrative assessment of regional ecosystem carrying capacity using above model is introduced. As an applied example, the assessment index system including 14 indexes and the standard including 3 levels are constructed for the East Dongting Lake wetland to assess its ecosystem carrying capacity. The result indicates that the ecosystem carrying capacity in studied area belongs to middle-load, which conforms to the local actual situation. In addition, RBF-ANN model is proved to be simple, effective to classify, with strong applicability and broadly-applicable prospect.
  • Keywords
    ecology; geophysics computing; lakes; radial basis function networks; East Dongting Lake wetland ecosystem; MATLAB tools; RBF-ANN; artificial neural network model; radial basis function artificial neural network; regional ecosystem; Artificial intelligence; Artificial neural networks; Automation; Computer networks; Ecosystems; Gaussian processes; Intelligent networks; Lakes; MATLAB; Mathematical model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
  • Conference_Location
    Hunan
  • Print_ISBN
    978-0-7695-3357-5
  • Type

    conf

  • DOI
    10.1109/ICICTA.2008.412
  • Filename
    4659467