CAREER: An Integrated Research/Educational Plan for a Grid-based Collaboratory to Support the Design and Management of Environmental Monitoring Systems

职业：基于网格的合作实验室的综合研究/教育计划，以支持环境监测系统的设计和管理

• 批准号: 0640443
• 负责人: Patrick Reed
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-15 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0640443&HistoricalAwards=false
• 关键词: CAREER; Integrated; Research; Educational; Plan

Reed, Patrick MPennsylvania State Univ University ParkProposal Number: 0640443CAREER: An Integrated Research/Educational Plan for a Grid-based Collaboratory to Support the Design and Management of Environmental Monitoring SystemsCynthia J. Ekstein, Program Director [phone]Problem: Long-term monitoring (LTM) design is a problem of paramount importance to the environmental engineering field because environmental observation data provide the sole means of assessing if engineered systems are successfully protecting human and ecologic health. LTM design is an extremely challenging problem, which requires engineers to capture an impacted system's governing processes, elucidate human and ecologic risks, limit management costs, and satisfy the interests of multiple stakeholders (e.g., site owners, regulators, and public advocates). In an effort to address these challenges, this proposed research and educational plan will develop the Adaptive Strategies for Sampling In Space and Time (ASSIST) collaboratory for the LTM community.Intellectual Merit: This research seeks to develop an open access monitoring framework that will allow users to combine a broader range of data sources with physical model predictions to improve spatiotemporal visualizations of impacted systems, reduce uncertainties, and decrease long-term management costs. To help ASSIST users balance these conflicting objectives, this proposed research will develop the first linkage-learning multiobjective genetic algorithm solver for grid computing environments. The multiobjective solver will be coupled with the C++ ASSIST Assimilation Toolbox to quantify monitoring design tradeoffs and provide spatiotemporal visualizations of their consequences. The C++ ASSIST Assimilation Toolbox will be developed using the Bayesian Maximum Entropy and Ensemble Kalman Filtering frameworks. The ASSIST collaboratory will enhance environmental engineers' abilities to (1) balance multiple design objectives, (2) merge high dimensional, nonlinear fate-and-transport model predictions with a broad range of data sources, (3) consider a much broader range of model and data uncertainties, and (4) adapt their objectives and system design to account for advances in real-time sensing. Three phases of testing and validation will be used to justify broad dissemination of the ASSIST collaboratory's decision support tools.Educational Merit: The ASSIST collaboratory will provide multi-media educational resources with interactive Microsoft Visual Basic software to help explain the underlying theory and implementation of the ASSIST framework's decision support tools. Classroom practices for incorporating the Microsoft Visual Basic educational software into undergraduate and graduate courses will be developed, assessed, and disseminated. Broader Impacts: The ASSIST decision support tools will be developed to maximize their ease-of-use in a wide array of water and environmental applications that require forecasting under uncertainty and/or multiobjective optimization (e.g., water distribution optimization under uncertainty, non-point source pollution management, water security, and multipurpose water systems control).

里德，帕特里克宾夕法尼亚州立大学公园提案编号： 0640443职业生涯：支持环境监测系统设计和管理的基于网格的合作实验室的综合研究/教育计划Cynthia J. Ekstein，项目总监（703）292- 7941问题：长期监测（LTM）对于环境工程领域来说，设计是一个至关重要的问题，因为环境观测数据提供了评估工程系统是否成功地保护人类的唯一手段。生态健康。LTM设计是一个极具挑战性的问题，它要求工程师捕捉受影响系统的管理过程，阐明人类和生态风险，限制管理成本，并满足多个利益相关者的利益（例如，网站所有者、监管机构和公共倡导者）。为了应对这些挑战，这项拟议的研究和教育计划将为LTM社区开发空间和时间采样的自适应策略（ASSIST）合作实验室。这项研究旨在开发一个开放访问监控框架，允许用户将更广泛的数据源与物理模型预测相结合，以改善受影响系统的时空可视化，减少不确定性，降低长期管理成本。为了帮助ASSIST用户平衡这些相互冲突的目标，本研究将开发第一个链接学习多目标遗传算法求解网格计算环境。多目标求解器将与C++ ASSIST同化工具相结合，以量化监测设计权衡，并提供其后果的时空可视化。C++ ASSIST同化系统将使用贝叶斯最大熵和Enclusive卡尔曼滤波框架开发。ASSIST合作实验室将提高环境工程师的能力，以（1）平衡多个设计目标，（2）将高维，非线性的命运和运输模型预测与广泛的数据源合并，（3）考虑更广泛的模型和数据不确定性，以及（4）调整他们的目标和系统设计，以适应实时传感的进步。三个阶段的测试和验证将被用来证明ASSIST合作实验室的决策支持工具的广泛传播。教育优点：ASSIST合作实验室将提供多媒体教育资源与交互式Microsoft Visual Basic软件，以帮助解释基本理论和ASSIST框架的决策支持工具的实施。将开发、评估和传播将Microsoft Visual Basic教育软件纳入本科和研究生课程的课堂实践。更广泛的影响：将开发ASSIST决策支持工具，以最大限度地提高其在各种水和环境应用中的易用性，这些应用需要在不确定性和/或多目标优化下进行预测（例如，不确定性下的水分配优化、非点源污染管理、水安全和多用途水系统控制）。