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
Link To Document :
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