• DocumentCode
    2773876
  • Title

    A prediction of groundwater quality using grey system neural network united model

  • Author

    Zhu, Changjun ; Chun Hao, Zhen ; Ju, Qin

  • Author_Institution
    Coll. of Urban Constr., Hebei Univ. of Eng., Handan, China
  • fYear
    2009
  • fDate
    17-19 June 2009
  • Firstpage
    3216
  • Lastpage
    3219
  • Abstract
    At present, classic methods are often used to predict groundwater level, but the result is not ideal. In view of the defect that the grey method can only predict the tendency approximately and artificial neural network can not predict the future tendency really, a new united grey neural network model was developed. This paper mainly analyses the groundwater quality and establishes their mathematical model based on the groundwater monitoring data of one area by united grey neural network method. It predicts various tendency of groundwater quality in this area in the future. Case study indicates that precision of the model is rather high and its popularization significance is better than the other models, and has some practical value when being used in the prediction of groundwater quality analysis.
  • Keywords
    condition monitoring; forecasting theory; grey systems; groundwater; neural nets; water supply; artificial neural network; grey system neural network united model; groundwater level prediction; groundwater monitoring data; groundwater quality prediction; mathematical model; united grey neural network model; Artificial neural networks; Differential equations; Educational institutions; Environmental management; Hydrology; Mathematical model; Monitoring; Neural networks; Predictive models; Water resources; RBF neural network; grey prediction; groundwater quality; united grey neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2009. CCDC '09. Chinese
  • Conference_Location
    Guilin
  • Print_ISBN
    978-1-4244-2722-2
  • Electronic_ISBN
    978-1-4244-2723-9
  • Type

    conf

  • DOI
    10.1109/CCDC.2009.5191466
  • Filename
    5191466