• DocumentCode
    588938
  • Title

    Application of Coupling Model with Neural Network and Projection Pursuit Based on Partial Least-Squares Regression to Water Resources Carrying Capacity Forecasting

  • Author

    Xiao-Yong Zhao

  • Author_Institution
    Coll. of Hydrol. & Water Resources, Hohai Univ., Nanjing, China
  • Volume
    2
  • fYear
    2012
  • fDate
    28-29 Oct. 2012
  • Firstpage
    446
  • Lastpage
    449
  • Abstract
    The method of partial least-squares regression can effectively deal with the problems of multicollinearity among independent variables", "but can not ideally solve the complicated problems of nonlinearity between dependent variables and independent variables. The method of coupling model with neural network and projection pursuit is an ideal tool to deal with the problem of nonlinearity, and it is very steady, but can not ideally solve the problems of multicollinearity among independent variables. The paper combines the two methods to establish the method of coupling model with neural network and projection pursuit based on partial least-squares regression for forecast water resources carrying capacity. the results of forecasting indicate that the combination is superior to either of them, the model was found to be able to give satisfactory effect.
  • Keywords
    forecasting theory; least squares approximations; neural nets; regression analysis; water resources; carrying capacity forecasting; coupling model; forecast water resources carrying capacity; independent variables; neural network; partial least-squares regression; projection pursuit; Correlation; Couplings; Fitting; Mathematical model; Polynomials; Predictive models; Water resources; coupling model with neural network and projection pursuit; partial least-squares regression; water resources carrying capacity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4673-2646-9
  • Type

    conf

  • DOI
    10.1109/ISCID.2012.261
  • Filename
    6406034