Abstract: It is well known that snowpack evolution depends on variety of landscape conditions including tree cover, slope, wind exposure, etc. In this presentation we report on methods that combine modern in-situ sensor technologies with machine learning-based algorithms to obtain improved models of snowpack evolution. Snowcloud is an embedded data collection system for snow hydrology field research campaigns that leverages distributed wireless sensor network technology to provide data at low cost and high spatial-temporal resolution. The system is compact thus allowing it to be deployed readily within dense canopies and/or steep slopes. The system has demonstrated robustness for multiple-seasons of operation thus showing it is applicable to not only short-term strategic monitoring but extended studies as well. We have used data collected by Snowcloud deployments to develop improved models of snowpack evolution using genetic programming (GP). Such models can be used to augment existing sensor infrastructure to obtain better areal snow depth and snow-water equivalence estimations. The presented work will discuss three multi-season deployments and present data (collected at 1-3 hour intervals and a multiple locations) on snowdepth variation throughout the season. The three deployment sites (Eastern Sierra Mountains, CA; Hubbard Brook Experimental Forest, NH; and Sulitjelma, Norway) are varied not only geographically but also terrain-wise within each small study area (~2.5 hectacre). We will also discuss models generated by inductive (GP) learning, including non-linear regression techniques and evaluation, and how short-term Snowcloud field campaigns can augment existing 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).