摘要
贝叶斯模型是降低设计洪水计算过程中线型选择不确定性的主要方法,其中,先验概率的确定对贝叶斯模型的建立至关重要。为了提高先验概率确定方法的普适性,本文以模型评价准则为基础,多方面考虑模型拟合特征,提出综合指数量化线型的拟合情况,并将其作为先验信息应用于贝叶斯模型,以便合理确定各线型的先验概率,进而达到降低线型选择不确定性影响的目的。研究结果表明:基于综合指数值来挖掘样本的先验信息,可获得较为可靠的先验概率值来合理预估后验概率,使得拟合效果较好的线型获得更高的权重;有先验信息的贝叶斯模型相比无先验信息的贝叶斯模型结果更优;耦合多个评价准则的综合指数值为先验概率的求解提供了新思路。
Bayesian model is a powerful tool for reducing model uncertainty in flood frequency analysis,and the key is how to calculate prior probability.To improve the universality of the calculation,first we work out a formula for calculating the comprehensive index and quantifying the effect of fitting used by different models based on model evaluation criteria.Then,this index is used in calculation of the prior probability adopted in our Bayesian model so as to reduce the uncertainty in model selection.Results show that the comprehensive index calculations lead to more reliable values of the prior probability that help improve the estimation of posterior probability,thereby reducing the uncertainty in flood frequency calculations.Compared with Bayesian model without such prior information,the present results are much better.A comprehensive index coupled with multiple evaluation criteria provides a new idea in determining prior probability.
作者
王义民
高盼星
郭爱军
畅建霞
赵明哲
WANG Yimin;GAO Panxing;GUO Aijun;CHANG Jianxia;ZHAO Mingzhe(State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China,Xi’an University of Technology,Xi’an 710048)
出处
《水力发电学报》
EI
CSCD
北大核心
2019年第7期67-76,共10页
Journal of Hydroelectric Engineering
基金
国家重点研发计划(2016YFC0400906)
国家自然科学基金(51879214)
关键词
设计洪水
洪水频率分析
贝叶斯模型
线型不确定性
先验概率
综合指数
design flood
flood frequency analysis
model uncertainty
Bayesian model
prior probability
comprehensive index
