Event Title

Session A4- Deployable model for understanding forecasting passage % in virtual reality during design: applications, strengths, and weaknesses

Location

UMass Amherst

Start Date

28-6-2011 10:20 AM

End Date

28-6-2011 10:40 AM

Description

Movement is a fundamental process in ecology, yet still poorly understood even as it is increasingly important for sustaining populations within increasingly fragmented landscapes. Animals may use a variety of sensory and cognitive means to obtain information about their environment and process it towards a decision. As these are becoming more clear to science we are integrating the underlying neurocognitive processes into engineering (hydrodynamic and water quality) decision–support models using an Eulerian-Lagrangian-agent Method (ELAM). The ELAM model works with any 2-D or 3-D mesh (e.g., any hydrodynamic and/or water quality model) and can use alternative algorithms for sensory perception and cognitive decision-making to drive individual movement behavior. We describe an ELAM currently used to quantitatively analyze past, observed fish movement behavior near hydropower dams and the ability to forecast plausible fish movement response to engineered hydraulic structures in virtual reality during design. We discuss ELAM applications, strengths, and weaknesses and how the method is being expanded to include feeding and bioenergetics towards describing habitat selection and growth potential impacted by water resource management over the spatiotemporal scales afforded by the latest 2-D and 3-D hydrodynamic and water quality models of river basins.

Comments

R. Andrew Goodwin (Andy) is a research environmental engineer at the U.S. Army Engineer R&D Center in Portland, Oregon. He obtained his Ph.D. from Cornell University in 2004 in Environmental Systems Engineering. His research focuses on integrating cognitive ecology (information processing and decision-making in animals) and sensory ecology (how animals obtain information about environmental patterns) into engineering (hydrodynamic and water quality) decision-support models using Eulerian-Lagrangian-agent Methods (ELAMs) to better understand how water resource management impacts fisheries movement, habitat selection, survival, and population abundance. His work has been mentioned in ‘Grand Challenges of the Future for Environmental Modeling’ prepared for the U.S. National Science Foundation (NSF). Dr. Goodwin’sresearch extends to network science (flow of information in swarms) and human movement behavior patterns using the disciplines of systems engineering, cognitive/sensory ecology, computational neuroscience, psychology, and numerical methods.

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Jun 28th, 10:20 AM Jun 28th, 10:40 AM

Session A4- Deployable model for understanding forecasting passage % in virtual reality during design: applications, strengths, and weaknesses

UMass Amherst

Movement is a fundamental process in ecology, yet still poorly understood even as it is increasingly important for sustaining populations within increasingly fragmented landscapes. Animals may use a variety of sensory and cognitive means to obtain information about their environment and process it towards a decision. As these are becoming more clear to science we are integrating the underlying neurocognitive processes into engineering (hydrodynamic and water quality) decision–support models using an Eulerian-Lagrangian-agent Method (ELAM). The ELAM model works with any 2-D or 3-D mesh (e.g., any hydrodynamic and/or water quality model) and can use alternative algorithms for sensory perception and cognitive decision-making to drive individual movement behavior. We describe an ELAM currently used to quantitatively analyze past, observed fish movement behavior near hydropower dams and the ability to forecast plausible fish movement response to engineered hydraulic structures in virtual reality during design. We discuss ELAM applications, strengths, and weaknesses and how the method is being expanded to include feeding and bioenergetics towards describing habitat selection and growth potential impacted by water resource management over the spatiotemporal scales afforded by the latest 2-D and 3-D hydrodynamic and water quality models of river basins.