• DocumentCode
    681272
  • Title

    GANN-based prediction of fresh water resources

  • Author

    Cuiyun Gao ; Linbo Jin ; Wanggen Wan ; Rui Wang

  • Author_Institution
    Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
  • fYear
    2013
  • fDate
    19-20 Aug. 2013
  • Firstpage
    79
  • Lastpage
    83
  • Abstract
    The issue of fresh water resources which has limited development in majority places is one of the most concerned focuses these days. This paper provides a novel approach to designing a specific and rational strategy for prediction of fresh water resources nationwide. The statistics across China are from Official Web sites. By comparing different methods including GM (1, 1), Logistic Regression Model and BP Neural Network, we establish a novel method named GANN which combines the strengths of GM and BP. Besides, WSI (Water Shortage Index) is created to represent the degree of water shortage. Also, experiments of different places are presented in our paper to prove our method.
  • Keywords
    backpropagation; environmental science computing; grey systems; neural nets; regression analysis; water resources; BP neural network; China; GANN-based fresh water resources prediction; GM (1, 1) method; WSI; logistic regression model; statistical analysis; water shortage degree; water shortage index; BP; Freshwater Withdrawals; GANN; Water Production Capacity; Water Shortage Index;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Smart and Sustainable City 2013 (ICSSC 2013), IET International Conference on
  • Conference_Location
    Shanghai
  • Electronic_ISBN
    978-1-84919-707-6
  • Type

    conf

  • DOI
    10.1049/cp.2013.2011
  • Filename
    6737793