• DocumentCode
    2070792
  • Title

    The prediction of soil moisture based on rough set-neural network model

  • Author

    Yan Wen ; Liu Wending ; Cheng Zhen ; Kan Jiangming

  • Author_Institution
    Acad. of Eng., Beijing Forestry Univ., Beijing, China
  • fYear
    2010
  • fDate
    29-31 July 2010
  • Firstpage
    2413
  • Lastpage
    2415
  • Abstract
    According to the analysis of meteorological parameters affecting the soil moisture, this paper puts forward a new prediction model of soil moisture based on rough set and neural network. Reduce the attribute of decision table and pick up key factors as input of artificial neural network. neural network is used as a function approximation to train the data set and build estimation model. It is shown that this hybrid method can reduce the training time, improve the learning efficiency, enhance the predication accuracy, and be feasible and effective.
  • Keywords
    decision tables; geophysics computing; geotechnical engineering; neural nets; rough set theory; soil; decision table attribute reduction; learning efficiency improvement; meteorological parameters; rough set neural network model; soil moisture prediction; training time reduction; Algorithm design and analysis; Artificial neural networks; Decision making; Irrigation; Predictive models; Soil moisture; Training; Neural Network; Rough Set; Soil Moisture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2010 29th Chinese
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-6263-6
  • Type

    conf

  • Filename
    5572030