research project details
Project Title: An Integrated Research/Educational
Plan for a Grid-based Collaboratory to Support the Design and Management of Environmental Monitoring
Systems
Investigator(s): Patrick Reed
Sponsor: National Science
Foundation
Abstract:
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:
- Balance multiple design objectives;
- Merge high dimensional, nonlinear fate-and-transport model predictions with a broad range of data sources;
- Consider a much broader range of model and data uncertainties; and
- 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).