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Collaborative Research: Multimodel Bayesian Data-Worth Analysis for Groundwater Remediation Design

合作研究：地下水修复设计的多模型贝叶斯数据价值分析

基本信息

批准号：
1552329
负责人：
Ming Ye
金额：
$ 27.16万
依托单位：
Florida State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552329&HistoricalAwards=false
关键词：
Collaborative Research Multimodel Bayesian Data

项目摘要

Groundwater contaminant remediation involves removing subsurface contaminants so that they do not pose unacceptable future risks to humans and the environment. While various remediation methods have been developed, they all face a common challenge that the groundwater environment is complex and cannot be fully understood and characterized with limited amount of time and resources. Hence, given the uncertainty in the characterization of the groundwater environment, the challenge is to design a remediation strategy that has the maximum probability of success or equivalently the minimum probability of failure. A common reason for remediation failure is ignoring model uncertainty. Using a single model for remediation design may lead to overconfidence in the predictive capability of the model and thus to increased probability of failure. The proposed research will reexamine the problem of remediation design under model uncertainty by using a multimodel-based data-worth analysis. In other words, multiple models are used to identify and guide the collection of the most valuable data for model evaluation, improvement, and reconstruction. Because of the synthesis between models, remediation designs, and data, the proposed multimodel data-worth analysis for remediation design will provide a transformative platform for scientists, engineers, and decision-makers to systematically investigate all components involved in groundwater remediation. This project will also provide an opportunity for interdisciplinary training of undergraduate and graduate students in the areas of hydrology, computational science, and civil engineering. In addition, the project will engage high school teachers and students in summer schools to gain laboratory and computational experience for understanding the concepts of groundwater contaminant transport and remediation. The proposed research has two objectives: to reformulate data-worth analysis for groundwater remediation with consideration of model uncertainty, and to break computational barriers between models and model analysis needed for remediation design. To achieve the first objective, a data-worth analysis will be integrated into a framework of multimodel analysis (also known as model averaging), which will be developed into a new procedure for remediation design that will be compatible with the multimodel data-worth analysis. To achieve the second objective, an accurate but cheap-to-evaluate surrogate of the models will be developed and then used for the data-worth analysis and remediation design under uncertainty. The Bayesian approaches (theoretical and computational) will be used for achieving both the objectives. While the proposed method of multimodel Bayesian data-worth analysis is general and can be applied to any remediation method, it will be integrated with the recently developed engineered injection and extraction method, a promising technique for in-situ remediation. The proposed methods will be evaluated in a two-prong strategy using synthetic and real-world modeling problems. The real-world problem involves uranium contamination at the Naturita Site, Colorado, and nitrogen contamination at the Indian River County, Florida. The synthetic study will mimic the real-world problem to the extent possible so that insights gained from the synthetic study can be used directly for the real-world modeling. This project will provide scientific support for on-going environmental remediation and monitoring at the two field sites as well as other contaminated sites.

地下水污染物补救涉及去除地下污染物，使其不会对人类和环境构成不可接受的未来风险。虽然已经开发了各种修复方法，但它们都面临着一个共同的挑战，即地下水环境是复杂的，并且在有限的时间和资源下无法完全理解和表征。因此，鉴于地下水环境特征的不确定性，所面临的挑战是设计一种补救战略，具有最大的成功概率或相当于最小的失败概率。补救失败的一个常见原因是忽略了模型的不确定性。使用单一模型进行补救设计可能会导致对模型预测能力的过度自信，从而增加失败的可能性。拟议的研究将重新审视模型不确定性下的修复设计问题，通过使用基于多模型的数据价值分析。换句话说，多个模型用于识别和指导收集最有价值的数据，以进行模型评估、改进和重建。由于模型，修复设计和数据之间的综合，拟议的多模型修复设计数据价值分析将为科学家，工程师和决策者提供一个变革性的平台，系统地调查地下水修复中涉及的所有组件。该项目还将为水文学、计算科学和土木工程领域的本科生和研究生提供跨学科培训的机会。此外，该项目将使高中教师和学生参加暑期学校，以获得实验室和计算经验，了解地下水污染物运输和补救的概念。建议的研究有两个目标：重新制定数据价值分析地下水修复模型的不确定性，并打破模型和模型分析修复设计所需的计算障碍。为实现第一个目标，将把数据价值分析纳入多模式分析框架（也称为模式平均），并将其发展成为一个与多模式数据价值分析兼容的新的补救设计程序。为了实现第二个目标，将开发一个准确但评估成本低廉的模型替代品，然后用于不确定情况下的数据价值分析和修复设计。贝叶斯方法（理论和计算）将用于实现这两个目标。虽然所提出的多模型贝叶斯数据价值分析方法是通用的，可以应用于任何修复方法，它将与最近开发的工程注入和提取方法，一个很有前途的原位修复技术相结合。所提出的方法将在一个双管齐下的战略，使用合成和现实世界的建模问题进行评估。现实世界中的问题涉及科罗拉多纳楚里塔场地的铀污染和佛罗里达印第安河县的氮污染。综合研究将尽可能地模拟现实世界的问题，以便从综合研究中获得的见解可以直接用于现实世界的建模。该项目将为两个现场以及其他受污染场地正在进行的环境补救和监测提供科学支持。