Uncertainty reduction and characterization for complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method
Uncertainty reduction and characterization for complex environmental fate and transport models: An empirical Bayesian framework incorporating the stochastic response surface method
复制标题
复杂环境命运和运输模型的不确定性减少和表征：结合随机响应面方法的经验贝叶斯框架
DOI：
10.1029/2002wr001810
复制
发表时间：
2003
影响因子：
5.4
通讯作者：
P. Georgopoulos
中科院分区：
文献类型：
--
作者：
Suhrid Balakrishnan;A. Roy;M. Ierapetritou;G. Flach;P. Georgopoulos
在这项工作中，已经开发了一个计算效率高的贝叶斯框架，用于减少和表征计算要求高的环境三维数值模型中的参数不确定性。该框架是基于随机响应面法（SRSM，它产生准确的和计算效率的统计等效简化模型）和马尔可夫链蒙特卡罗方法的联合应用。被选来演示这一框架的应用程序涉及美国能源部萨凡纳河站点一般分离区的稳态地下水流，使用地下水流和污染物传输（FACT）代码建模。输入参数的不确定性，最初的专家意见的基础上，被发现减少后验分布的所有变量。然后，将得到的联合后验分布进一步用于河流底流和井位水头值的最终不确定性分析。
In this work, a computationally efficient Bayesian framework for the reduction and characterization of parametric uncertainty in computationally demanding environmental 3‐D numerical models has been developed. The framework is based on the combined application of the Stochastic Response Surface Method (SRSM, which generates accurate and computationally efficient statistically equivalent reduced models) and the Markov chain Monte Carlo method. The application selected to demonstrate this framework involves steady state groundwater flow at the U.S. Department of Energy Savannah River Site General Separations Area, modeled using the Subsurface Flow And Contaminant Transport (FACT) code. Input parameter uncertainty, based initially on expert opinion, was found to decrease in all variables of the posterior distribution. The joint posterior distribution obtained was then further used for the final uncertainty analysis of the stream base flows and well location hydraulic head values.