Uncertainty Analysis of Flood Forecasting in River Channel

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Abstract:

In this paper, the stochastic differential equations theory was used to analyze the uncertainty of flood forecasting in river channel based on the forward algorithm of linear characteristic. And then a river channel flood forecasting model, in which the coefficient of storage and discharge was regarded as a random variable, was built. The statistical characteristics of outflow process could be taken part in theory by the built river channel flood forecasting model when the coefficient of storage obeyed a kind of normal distribution. Storage coefficient is random variable in the model. The results showed that the uncertainty degree of outflow process could be made through considering the uncertainty of river channel flood forecasting, which would provide some references for making decision in flood control.

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Periodical:

Advanced Materials Research (Volumes 550-553)

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2489-2492

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Online since:

July 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] RuiXiaofang. Principles of Hydrology. Beijing: China water power Press. 2008, 4.

Google Scholar

[2] Xing Zhenxiang. Bayesian probabilistic forecasting based on certain hydrological models. Hohai University, 2007,7.

Google Scholar

[3] Shen Bing. Principles of Hydrology. Beijing: China water power Press. 2008,9.

Google Scholar

[4] Kuczera,G. Improved parameter inference in catchment models: 1,Evaluating parameter uncertainty. Water Resources Research,1983,19(5) , pp.1151-1162.

DOI: 10.1029/wr019i005p01151

Google Scholar

[5] Zhang Honggang, GuoShenglian, He Xinlin. Recent Advancement and Prospect of Hydrological Forecasting Uncertainty Study. Journal of Shihezi University (Natural Science), 2006, 24(1),pp.15-21.

Google Scholar

[6] Yevjevich, V. Stochastic Process in Hydrology. Water Resources Publications, 1972.

Google Scholar