University of Vermont

Cosmogenic Nuclide Laboratory and Geomorphology Research Group

Lance Besaw



Lance's research at UVM focuses on using advanced computational methods for solving environmental engineering problems. His current research includes investigating the use of artificial neural networks for predicting stream discharge using precipitation and temperature as predictor variables. This research project is focusing on the more than 70 years of recorded discharge in the Winooski River basin, Vermont.
PhD Dissertation (expected 2009)
Ph.D. Dissertation focus: Application of artificial neural networks in solving civil and environmental engineering problems
Master's Degree (University of Vermont, 2006)
M.S. Civil and Environmental Engineering, Parameter Estimation and Conditional Simulation using a Counterpropagation Artificial Neural Network
Undergraduate Degree
University of Vermont, B.S. Civil and Environmental Engineering, 2004, Emphasis in Water Quality, Groundwater, Surface Water and Wastewater
Projects
Modeling and Analyzing Flow History of the Winooski River
Email Address
lbesaw@cems.uvm.edu
Current Position and Contact Information (9/2008)
Ph.D. Candidate
UVM School of Engineering
University of Vermont
33 Colchester Ave
213 Votey Building
Burlington VT, 05405
802.656.4595
Publications Based on UVM Research

Besaw, L. E., and D. M. Rizzo, 2007, Stochastic simulation and spatial estimation with multiple data types using artificial neural networks, Water Resources Research, 43, W11409, doi:10.1029/2006WR005509.
First-Authored Meeting Abstracts Based on UVM Research

Besaw, L.E., Keith Pelletier, Donna M. Rizzo, Leslie Morrissey and Mike Kline, 2008, Advances in watershed management and fluvial hazard mitigation using artificial neural networks and remote sensing, R. W. Babcock Jr. and R. Walton (editors), ASCE 2008 World Water & Environmental Resources Congress, Environmental and Water Resources Institute, Honolulu, HI, May 2008.

Besaw, L.E., Rizzo, D.M., Bierman, P., and Hackett, W.R., 2008, Daily streamflow forecasting with Artificial Neural Networks: Application in the Winooski River basin, Vermont: EOS Transactions of the American Geophysical Union. (download pdf)

Besaw L.E., D.M. Rizzo, M. Kline, 2007, Artificial Neural Networks for the Prediction of Channel Geomorphic Condition and Stream Sensitivity. C. Kabbas (editors). ASCE 2007 World Water & Environmental Resources Congress, Environmental and Water Resources Institute, Tampa Bay, FL, May 2007.
Other Meeting Abstracts Based on UVM Research

Hackett, W.R., Bierman, P. R., Rizzo, D.M. and Besaw, L.E., 2009, Increasing precipitation and runoff interact with land use change over the last 70 years in the Winooski River basin, northern Vermont, WRCC annual meeting, Amherst, Massachusetts (download pdf)

Hackett, W.R., Bierman, P.R., Rizzo, D.M., and Besaw, L.E., 2008, Analysis of changing climate and hydrology in the Winooski River Basin, Vermont, EPSCoR annual meeting. Burlington, VT. (download abstract pdf) (download poster)

Hackett, W.R., Bierman, P.R., Rizzo, D.M., and Besaw, L.E., 2008, Increasing precipitation and runoff over the last 70 years, the Winooski River Basin, Vermont: Geological Society of America Abstracts with Programs, p. 301-1. (download pdf)

Last modified February 04 2009 02:40 PM

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