HR: 1340h
AN: H23A-1125    [Abstracts]
TI: Improved Site Characterization Using Microbiological Community Profiles from Landfill-leachate Contaminated Groundwater and Artificial Neural Networks
AU: * Rizzo, D M
EM: drizzo@emba.uvm.edu
AF: University of Vermont, Department of Civil & Environmental Engineering, 213 Votey Building, Burlington, VT 05405 United States
AU: Mouser, P J
EM: Paula.Mouser@uvm.edu
AF: University of Vermont, Department of Civil & Environmental Engineering, 213 Votey Building, Burlington, VT 05405 United States
AU: Besaw, L
EM: lbesaw@emba.uvn.edu
AF: University of Vermont, Department of Civil & Environmental Engineering, 213 Votey Building, Burlington, VT 05405 United States
AB: Microbiological profiles and general water quality were sampled from groundwater monitoring wells surrounding a leaky municipal landfill in northeastern New York. Microbial samples were analyzed using polymerase chain reaction and gel electrophoresis, and water quality was tested for pH, temperature, redox, turbidity, and specific conductance. The bacterial profiles for each sample location were incorporated into an artificial neural network for the purpose of improving site characterization using multiple types of data. A classification artificial neural network was trained to predict the water quality based on the microbial profile by detecting relationships and structure between samples. Additional site data was used for testing and validation of the network
DE: 3230 Numerical solutions
DE: 1829 Groundwater hydrology
DE: 0400 Biogeosciences
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting