水动力-水质耦合模型污染源识别的贝叶斯方法

朱嵩; 刘国华; 王立忠

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水动力-水质耦合模型污染源识别的贝叶斯方法

水利与土木工程 | 更新时间：2023-11-28

- 水动力-水质耦合模型污染源识别的贝叶斯方法
- A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics
- 工程科学与技术 2009年41卷第5期页码：30-35
- 作者机构：
  
  1. 浙江大学建筑工程学院
  2. 浙江大学建筑工程学院浙江,杭州,310027
- 作者简介：
- 基金信息：
  
  国家重点基础研究发展计划(2005CB724202);国家自然科学基金项目(50609024);浙江省自然科学基金(Y506138)
- DOI：
  中图分类号：
- 纸质出版日期：2009，
  
  网络出版日期：2008-11-19，
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朱嵩,刘国华,王立忠.水动力-水质耦合模型污染源识别的贝叶斯方法[J].工程科学与技术,2009,41(5):30-35.

Zhu Song, LIU Guo-hua, 王立忠. A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics[J]. Advanced Engineering Sciences, 2009,41(5):30-35.
朱嵩,刘国华,王立忠.水动力-水质耦合模型污染源识别的贝叶斯方法[J].工程科学与技术,2009,41(5):30-35. DOI：

Zhu Song, LIU Guo-hua, 王立忠. A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics[J]. Advanced Engineering Sciences, 2009,41(5):30-35. DOI：

摘要

中文摘要: 环境水力学系统的诸多不确定性（如测量数据的不确定性等），导致水体中污染源识别这一类反问题具有不适定性，尤其表现为反演结果的非唯一性。经典的正则化方法和最优化方法由于只能获得参数的“点估计”，因而在求解不确定性较强的问题时存在较大的困难。同时水质模型和流场控制方程（Navier-Stokes方程）耦合，使得正问题的解具有较强的非线性特征。为了解决上述问题，本文针对水动力-水质耦合模型，建立了基于贝叶斯推理的污染物点源识别的数学模型，通过马尔科夫链蒙特卡罗(Markov chain Monte Carlo

MCMC)后验抽样获得了污染源位置和强度的后验概率分布和估计量，较好地处理了模型的不确定性和非线性。算例结果表明，结合MCMC抽样的贝叶斯推理方法能很好地描述、求解水动力-水质耦合场条件下的污染源识别反问题。

Abstract

Abstract:There lies much uncertainty in the environmental hydraulics system

such as the uncertainty of the measurement data

therefore the kind of pollution source identification problem is ill-posed

especially the non-unique. The classic regularization method and the optimization method can only get the “point estimation” of the parameter

so it is hard for them to solve the problem with more uncertainty. Since the water quality model is coupled with the flow field equation(Navier-Stokes equation)

the direct problem is much nonliear. In order to settle the above difficulties

for the hydrodynamics-water quality coupled model

a pollution point source identification model is advanced based on Bayesian inference. Markov chain Monte Carlo sampling method is used to get the posterior probabilty distribution of the source’s position and intensity

thus solving the uncertainty and the nonlinearity well. Computational case’s result indicates the Bayesian inference with MCMC sampling can describe and solve the pollution source identification inverse problem for the hydrodynamics-water quality coupled model better.

关键词

环境水力学反问题贝叶斯推理污染源识别

Keywords

environmental hydraulicsinverse problemBayesian inferencepollution source identification

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