• DocumentCode
    1897574
  • Title

    Neural Network Model on Basin Flood Prevention Effect Assessment

  • Author

    Shi, Y.Z. ; Zheng, Y.Q. ; Li, Miao

  • Author_Institution
    Sch. of Water Conservancy, Changsha Univ. of Technol. & Sci., Changsha, China
  • Volume
    1
  • fYear
    2009
  • fDate
    10-11 Oct. 2009
  • Firstpage
    83
  • Lastpage
    86
  • 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 scholars´ attention. Its framework, design, simulation and output of graphs are presented. With the aid of MATLAB tools, integrative assessment of basin flood prevention effect using above model is introduced. As an applied example, the assessment index system including 15 indexes and the standard including 3 levels are constructed for Liaohe basin to assess its flood prevention effect. The result indicates that the flood prevention effect in studied area belongs to middle, 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
    floods; geophysics computing; hydrological techniques; neural nets; Liaohe basin; MATLAB tools; RBF-ANN model; assessment index system; basin flood prevention effect assessment; integrative assessment; neural network model; radial basis function artificial neural network; Artificial neural networks; Automation; Computer networks; Control systems; Floods; Intelligent networks; Mathematical model; Neural networks; Safety; Water conservation; Liaohe basin; assessment; flood prevention effect; model; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
  • Conference_Location
    Changsha, Hunan
  • Print_ISBN
    978-0-7695-3804-4
  • Type

    conf

  • DOI
    10.1109/ICICTA.2009.29
  • Filename
    5287703