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

GSTDTAP

项目编号	1552329
	Collaborative Research: Multimodel Bayesian Data-Worth Analysis for Groundwater Remediation Design
	Ming Ye
主持机构	Florida State University
项目开始年	2016
	2016-08-01
项目结束日期	2019-07-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	271601(USD)
国家	美国
语种	英语
英文摘要	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.
来源学科分类	Geosciences - Earth Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/69955
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Ming Ye.Collaborative Research: Multimodel Bayesian Data-Worth Analysis for Groundwater Remediation Design.2016.