• DocumentCode
    507762
  • Title

    The Risk Neural Network Based Visibility Forecast

  • Author

    Wang, Kai ; Zhao, Hong ; Liu, Aixia ; Bai, Zhipeng

  • Author_Institution
    Coll. of Environ. Sci. & Eng., Nankai Univ., Tianjin, China
  • Volume
    1
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    338
  • Lastpage
    341
  • Abstract
    Currently, the measurement of the visibility mainly depends on the human eyes, so that the objectivity is relatively poor. In general, the higher the visibility is, the greater the measurement error is. On the other hand, in practice, as opposed to high visibility situations, low visibility situation is more notable. Therefore, for the same forecast error, low visibility should have a higher risk value. Based on this principle, a risk neural network model is proposed, in which the relatively high risk is given for the low visibility case, while the relatively low risk is given for the high-visibility case. Experimental results show that the risk neural network model is superior to the standard one and linear regression model, which provide a support for our work.
  • Keywords
    forecasting theory; neural nets; regression analysis; risk analysis; forecast error; linear regression model; measurement error; risk neural network based visibility forecast; Artificial intelligence; Biological neural networks; Biological system modeling; Brain modeling; Educational institutions; Humans; Neural networks; Pollution; Predictive models; Weather forecasting; forecast; neural network; visibility;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.152
  • Filename
    5362939