DocumentCode :
3272836
Title :
Monthly discharge prediction with 2D cloud model in non-dry season
Author :
Li Fa-wen ; Zhang Chao
Author_Institution :
Sch. of Civil Eng., Tianjin Univ., Tianjin, China
fYear :
2011
fDate :
15-17 April 2011
Firstpage :
4284
Lastpage :
4287
Abstract :
The cloud model is transformation model between qualitative concepts and quantitative expression, the qualitative concept is represented by expected value, entropy and hyper entropy in this mode. It combines the fuzziness with randomness, and possesses strong robustness for the uncertain question. Considering the main influence of factors, the 2D cloud model is applied to the prediction of monthly discharge in non-dry season, this mechanism can effectively overcome the fuzziness and the random in monthly discharge prediction, and can offer a novel approach for monthly discharge prediction. The results show that this method has excelled precision, it is also in accord with the real ones.
Keywords :
clouds; 2D cloud model; expected value; fuzziness; hyper entropy; monthly discharge prediction; nondry season; qualitative concept; quantitative expression; randomness; robustness; transformation model; Computational modeling; Data mining; Discharges; Entropy; Helium; Predictive models; Water resources; 2D cloud model; monthly discharge; prediction; randomness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
Type :
conf
DOI :
10.1109/ICEICE.2011.5777234
Filename :
5777234
Link To Document :
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