Abstract: Sustainable water resources management is critical in both developing and established communities; particularly with the challenges associated with surface and groundwater contamination and potential precipitation shifts resulting from climate change. We test and develop a method for forecasting short-term (daily) streamflow using data-driven Artificial Neural Networks (ANNs) to efficiently manage water resources during times of shortage and provide improved flood mitigation strategies. In this work, a generalized regression neural network is combined with a counterpropagation network (GRNN and CPN respectively). This hybrid network and its individual constituents are compared with traditional temporal forecasting methods (e.g. multiple linear regression and auto-regressive moving averaging or ARMA). Model inputs consist of antecedent precipitation, temperature and discharge, while the output is river discharge in space through time. A hierarchy of ANNs has been implemented to capture the spatial characteristics of this complex river network. In the network hierarchy, predicted discharge from upstream (or lower order stream) networks is used as inputs (in addition to climatic variables) to downstream (higher order) networks. A semi- variogram analysis is used to estimate the temporal lag between input and output variables. The methods are implemented on the 2,704 km2 Winooski River basin in Vermont. Discharge records from six USGS stream gage stations and eight weather stations within the basin provide the training, cross-validation and prediction datasets for this application. Initial predictive results indicate the ANN methods compare well with those published in the literature and outperform traditional methods.
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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).