Improving Calibration, Sensitivity and Uncertainty Analysis of Data Based Models of the Environment

改进基于数据的环境模型的校准、灵敏度和不确定性分析

• 批准号: 0229176
• 负责人: Christine Shoemaker
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0229176&HistoricalAwards=false
• 关键词: Improving; Calibration; Sensitivity; Uncertainty; Analysis

0229176 Shoemaker Site-specific pollution transport models are based on field data and the calibration of the parameter values that cannot be directly measured. This combination of field data and calibrated model is a powerful tool in environmental analysis. However, one of the difficulties in using such models is that the calibration process can be computationally very demanding if it is done thoroughly. An even more serious computational obstacle is the quantification of the uncertainty associated with model forecasts of pollution transport.This proposal develops a new computational procedure (acronym ROCUS) that can be applied to a range of pollution transport problems. In the proposed research, ROCUS will be applied to a large watershed in New York, for which the PI's research model results are currently being used in formulating regulatory policy. ROCUS will also be used for anaerobic bioremediation of chlorinated ethenes using field data collected by DOD and an 18 species reactive transport model developed by the PI and her students.The ROCUS procedure involves a group of computationally efficient algorithms for calibration, combined sensitivity and uncertainty analysis that is integrated through the use of response surface methods. One of the hypothesis of the study is that computation time to find good values for model calibration can be significantly reduced by replacing methods currently in use with one or both of the two new optimization algorithms to be investigated. These two methods both involve the use of a Radial Basis Function response surface coupled with a new optimization algorithm. Previously published papers and the PI's computational experiments support this hypothesis. The extent to which this hypothesis is valid will be assessed in the proposed research by a systematic comparison of accuracy and computational speed of the two response surface optimization algorithms with two existing heuristic methods. These tests will be performed on the two environmental applications and on standard global optimization test problems.The applications represented are very serious pollution problems, and the results will add to scientific understanding and to methods for environmental protection. The teaching and research objectives will be obtained by incorporation of research results into two courses the PI teaches. Research infrastructure contributions will follow from the interdisciplinary nature of the research. In addition, the PI has a long history of working to promote underrepresented groups, especially women engineers, and this research project will continue that effort.

0229176 Shoemaker 特定地点的污染输送模型基于现场数据和无法直接测量的参数值的校准。现场数据和校准模型的结合是环境分析的强大工具。然而，使用此类模型的困难之一是，如果彻底完成校准过程，计算量可能会非常大。一个更严重的计算障碍是与污染传输模型预测相关的不确定性的量化。该提案开发了一种新的计算程序（缩写ROCUS），可应用于一系列污染传输问题。在拟议的研究中，ROCUS将应用于纽约的一个大流域，PI的研究模型结果目前正用于制定监管政策。 ROCUS 还将使用国防部收集的现场数据和 PI 及其学生开发的 18 种反应传输模型，用于氯化乙烯的厌氧生物修复。ROCUS 程序涉及一组计算高效的校准算法、组合灵敏度和不确定性分析，通过使用响应面方法进行集成。该研究的假设之一是，通过将当前使用的方法替换为要研究的两种新优化算法中的一种或两种，可以显着减少找到模型校准的良好值的计算时间。这两种方法都涉及使用径向基函数响应面和新的优化算法。先前发表的论文和 PI 的计算实验支持了这一假设。在所提出的研究中，将通过对两种响应面优化算法与两种现有启发式方法的精度和计算速度进行系统比较来评估该假设的有效性程度。这些测试将针对两个环境应用和标准全局优化测试问题进行。所代表的应用是非常严重的污染问题，其结果将增加对环境保护的科学认识和方法。教学和研究目标将通过将研究成果纳入PI教授的两门课程中来实现。研究基础设施的贡献将源于研究的跨学科性质。此外，PI 长期以来一直致力于促进代表性不足的群体，特别是女性工程师的发展，本研究项目将继续这一努力。