Donna Rizzo’s Research Group at the University of Vermont

Dr. Rizzo’s research uses geostatistics and computational techniques, including artificial neural networks (ANNs), to solve problems in Civil and Environmental Engineering.

Figure 1: From Left to Right: Jeffrey Doris, Donna Rizzo, Paula Mouser, and Lance Besaw.

Links

NSF Environmental Biology Undergraduate Mentoring Group

CEE 125 Engineering Economics Home Page

Selected Publications

Stevens, L., D.M. Rizzo, and A. Prenowitz. “The Effect of Spatial Variation in Pathogen Distribution and Abundance on Local Adaptation”. The Royal Society. (Submitted).

Mouser, P.J. W.C. Hession, D.M. Rizzo, and N.J. Gotelli. “Hydrology and geostatistics in Vermont, U.S.A. kettlehole peatland”. Journal of Hydrology. (In press).

Doris, J.J., and D.M. Rizzo, “A Watershed Classification System using Hierarchical Artificial Neural Networks for Diagnosing Watershed Impairment at Multiple Scales”, ASCE 2004 World Water & Environmental Resources Congress, Salt Lake City, UT, June, 2004.

Mouser, P.J. and D.M. Rizzo, “A Long-term monitoring implications using combined geostatistics of hydrochemistry and microbial community fingerprinting at waste disposal sites”, ASCE 2004 World Water & Environmental Resources Congress, Salt Lake City, UT, June, 2004.

Rizzo, D.M. “Field Studies of Long-term Monitoring Design”, Book Chapter in Long-Term Groundwater Monitoring: The State of the Art. Prepared by The Task Committee on the State of the Art in Long-Term Groundwater Monitoring Design, ASCE, 2003.

Rizzo, D.M. and Suzanne Conklin, “Using Artificial Neural Networks to Predict Local Disease Risk Indicators with Multi-Scale Weather, Land & Crop Data”, ASCE 2003 World Water & Environmental Resources Congress, Philadelphia, PA, June, 2003.

Underwood K. and D.M. Rizzo, “Modeling of Sediment Transport in Geomorphically Unstable Alluvial Channels Using ANNs”, ASCE World Water & Environmental Resources Congress, PA, 2003.

Rizzo, D.M., D.E. Dougherty and M. Yu, “An Adaptive Monitoring and Operations System (aLTMOsTM) for Environmental Management”, ASCE Joint Conference on Water Resources Engineering and Water Resources Planning & Management, Minneapolis, MN, August 2000.

Rizzo, D.M. and D.E. Dougherty, “Artificial Neural Networks in Subsurface Characterization”, Book Chapter in Artificial Neural Networks in Hydrology, R.S. Govindaraju and A.R. Rao (eds.), pp. 111-133, 2000.

Rizzo, D.M. and D.E. Dougherty, “Application of Artificial Neural Networks for Site Characterization Using ‘Hard’ and ‘Soft’ Information”, in (Peters et al., Eds.) International Computational Methods in Water Resources X, Vol. 1, Kluwer Academic Pubs., pp. 793-799, Germany, 1994.

Rizzo, D.M. and D.E. Dougherty, “Characterization of aquifer properties using artificial neural networks: Neural Kriging”, Water Resources Research, 30 (2), pp. 483-497, 1994.

Rizzo, D.M. and D.E. Dougherty, “Design Optimization for Multiple Management Period Groundwater Remediation”, Water Resources Research, 32 (8), pp.2549-2561, 1996.