A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics

Zhu Song; LIU Guo-hua; 王立忠

您当前的位置：

首页 >

文章列表页 >

A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics

更新时间：2023-11-28

- A Bayesian approach for identification of the pollution source in water quality model coupled with hydrodynamics
- Advanced Engineering Sciences Vol. 41, Issue 5, Pages: 30-35(2009)
- 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：
- DOI：
  CLC：
- Published：2009，
  
  Published Online：19 November 2008，
扫描看全文
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 41(5):30-35(2009)
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 41(5):30-35(2009) 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

references

朱嵩基于贝叶斯推理的环境水力学反问题研究 [D].

Hadamard J,Lectures on the Cauchy problems in linear partial differential equations,New Haven CT:Yale University Press,1923.

曹叔尤.水力学及河流动力学基本问题研究的现状与任务[J].四川大学学报(工程科学版),2002(1)

Jingbo Wang ；Nicholas Zabaras,A Markov random field model of contamination source identification in porous media flow,International Journal of Heat and Mass Transfer ,2006, 49(5/6).

Snodgrass M F;Kitanidis P K,A geostatistical approach to contaminant source identification,Water Resources Research

Tarantola A,Inverse problem theory and methods for model parameter estimation,Philadelphia:SIAM,2004.

Kaipio J;Somersalo E,Statistical and computational inverse problem,New York:springer-verlag,2004.

Ling, L ；Yamamoto, M ；Hon, YC ；Takeuchi, T,Identification of source locations in two-dimensional heat equations,Inverse Problems ,2006, 22(4).

Volkan Akcelik ；George Biros ；Omar Ghattas ；Kevin R. Long ；Bart van Bloemen Waanders,A variational finite element method for source inversion for convective-diffusive transport,Finite Elements in Analysis and Design ,2003, 39(8).

闵涛;周孝德;张世梅.对流-扩散方程源项识别反问题的遗传算法[J].水动力学研究与进展A辑,2004(4)

戴会超;王玲玲.工程水力学反问题研究及应用[J].四川大学学报(工程科学版),2006(1)

Andrieu C;Freitas N D;Doucet A;Jordan M,An introduction to MCMC for machine learning,Journal of Machine Learning Research

更多指标>

Views

298

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of Bayesian Inference to Estimate the Parameters in 2D Convection diffusion Equation with Source

Identification of Parameters for Standard k-ε Turbulence Model Based on Bayesian Inference

Related Author

No data

Related Institution

浙江大学水利与海洋工程学系浙江

广东省电力设计研究院

浙江大学建工学院

清华大学水利水电工程系

⁰