• DocumentCode
    2302380
  • Title

    Prediction of Seepage Quantities of Earthfill Dam Foundation Based on Artificial Neural Network

  • Author

    Peng, Hui ; Tian, Bin

  • Author_Institution
    Key Lab. of Geol. Hazards on Three Gorges Reservoir Area, China Three Gorges Univ., Yichang, China
  • Volume
    2
  • fYear
    2010
  • fDate
    13-14 March 2010
  • Firstpage
    919
  • Lastpage
    922
  • Abstract
    In artificial neural network(ANN)method, the information treatment of the network are finished through interaction of neurones of the network. There are a series of advantages in the methodology, such as high degree non-linear, self-adaptation, self-learning, etc. Therefore the ANN method is used widely in the fields of prediction of physical quantities. In most cases, seepage equations show strong non-linear characteristics. This paper presents and establishes an ANN model based on the training method of learning into groups. Combining the practice of Xixia Researvoir, application of the ANN model to prediction of seepage quantities of the dam foundation is studied. There are high degree accuracy in the prediction result through using the ANN method. The results demonstrate that this method is widely available for the fields of dam safety monitoring and operation.
  • Keywords
    neural nets; water supply; ANN; Xixia Researvoir; artificial neural network; earthfill dam foundation; information treatment; nonlinear degree; safety monitoring; seepage equations; seepage quantities prediction; self adaptation; self learning; Accuracy; Area measurement; Artificial neural networks; Educational technology; Geologic measurements; Monitoring; Neurons; Nonlinear equations; Predictive models; Safety; earthfill dam; learning into groups; neural networks; prediction; seepage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
  • Conference_Location
    Changsha City
  • Print_ISBN
    978-1-4244-5001-5
  • Electronic_ISBN
    978-1-4244-5739-7
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2010.273
  • Filename
    5459983