Integrated Statistical and Optimization Analysis for Computationally Expensive Models of Complex Environmental Systems

研究人员将开发一种综合的、计算效率高的通用方法，利用现场和实验室数据以及环境模型，为将参数和输入值的不确定性纳入与环境分析、保护和补救有关的科学调查和决策提供特定地点的基础。 该研究将使用最近开发的基于响应面的全局优化方法，该方法提供了一种有效的方法，用于校准模型参数时，作为典型的复杂环境模型，该模型是昂贵的运行。效率（计算和统计）也将通过在贝叶斯框架内使用反映数据中的非正态分布、异方差和相关性的数据转换和权重来改进，以解决不同来源的不确定性。环境系统的未来状态将通过模拟基本环境模型来预测，其中一些参数是根据环境系统的后验分布选择的。参数值，并使用每个感兴趣的输出变量的响应曲面近似。 将进行灵敏度和不确定性分析，并调查其准确性。 该研究将应用于纽约的Cannonsville水库和用于饮用水的地下水中有毒氯化乙烯的生物修复。

The investigators will develop an integrated and computationally efficient general method for utilizing field and laboratory data coupled with environmental models to provide a site-specific basis for incorporating uncertainty in parameter and input values into scientific investigations and decisions related to environmental analysis, protection, and remediation. The research will use recently developed methods of global optimization based on response surfaces that provide an efficient method for calibration of model parameters when, as is typical for complex environmental models, the model is expensive to run. Efficiency (computational and statistical) will also be improved by using, within a Bayesian framework, data transformations and weightings that reflect nonnormal distributions, heteroscedasticity, and correlation in the data, to address different sources of uncertainty.Future states of the environmental system will be forecast by simulating the underlying environmental model for a number of sets of parameters selected according to the posterior distribution of parameter values and using a response surface approximation of each output variable of interest. Sensitivity and uncertainty analyses will be done and their accuracies will be investigated. The research will be applied to the Cannonsville reservoir in New York and the bioremediation of toxic chlorinated ethenes in groundwater used for drinking water.