Abstract: Groundwater remediation management does not end with the installation of the cleanup system and flipping the switch. Water quality samples from groundwater monitoring wells are used to verify that the remediation plan is working as planned, and to ensure that there are no unexpected impacts or risks. An important component of the adaptive long-term monitoring and operation system package aLTMOs is a data-driven Bayesian filter, a data assimilation technique that integrates monitoring data and phenomenological modeling results to provide insightful analyses on plume dynamics in both spatial and temporal dimensions. In this process, a model prediction is calculated using the flow and transport simulation. When new measurements are made, they are used systematically to adjust the model prediction. Irregular spatial and temporal data collection can be incorporated. Concentration estimates and their uncertainties are updated as monitoring data are collected. Based on uncertainty analysis in spatial and temporal dimensions and cleanup goals, an optimal monitoring plan is selected. This paper uses a real superfund site problem to demonstrate the application of the data-driven Bayesian modeling technique in uncertainty analysis and redundant sampling reduction optimization in groundwater long term monitoring design.
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).