Clustering stochastic point process model for flood risk analysis |
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| Authors: | Z X Xu J Y Li and K Ito |
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| Institution: | (1) R & D Headquarters, CTI Engineering Co., Ltd. (Visiting Researcher, from Tsinghua Univ., P. R. of China), Tokyo 103, Japan, JP;(2) The People's University of China, Beijing 100872, China, CN;(3) CTI Engineering Co., Ltd., Tokyo 103, Japan, JP |
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| Abstract: | Since the introduction into flood risk analysis, the partial duration series method has gained increasing acceptance as an
appealing alternative to the annual maximum series method. However, when the base flow is low, there is clustering in the
flood peak or flow volume point process. In this case, the general stochastic point process model is not suitable to risk
analysis. Therefore, two types of models for flood risk analysis are derived on the basis of clustering stochastic point process
theory in this paper. The most remarkable characteristic of these models is that the flood risk is considered directly within
the time domain. The acceptability of different models are also discussed with the combination of the flood peak counted process
in twenty years at Yichang station on the Yangtze river. The result shows that the two kinds of models are suitable ones for
flood risk analysis, which are more flexible compared with the traditional flood risk models derived on the basis of annual
maximum series method or the general stochastic point process theory.
Received: September 29, 1997 |
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| Keywords: | : Risk clustering point process Poisson flood |
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