Classification ANNs to Support Modeling of Sediment Transport in Geomorphically Unstable Alluvial Channels
Protection and Restoration of Urban and Rural Streams Symposium, , , 2004
Abstract: Two artificial neural networks (ANNs) have been developed for the classification of reach-scale vulnerability and channel evolution stage in self-formed alluvial channels utilizing existing data for Vermont watersheds. Optimal land use management to minimize sediment transport is presently constrained by limited capacity of existing watershed models to accurately simulate the dynamic processes of channel adjustment at varying temporal and spatial scales. Stakeholders are in need of tools for the visualization and communication of complex cause-and-effect relationships of channel adjustment processes in response to diffuse and point-source Stressors including increased percent imperviousness, channelization, and gravel extraction. The vulnerability and condition ANNs developed in this study will serve as modules in a hierarchical suite of ANNs to enhance existing geographic information system (GIS)-based watershed models for the prediction of watershed and subarea sensitivity to natural and anthropogenic Stressors. Enhanced predictive modeling will serve land use management at the watershed scale to minimize clean and polluted sediment mobilization to receiving waters and reduce loss of agricultural lands and infrastructure.
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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).