HR: 0800h
AN: H51B-0350    [Abstracts]
TI: Data Sufficiency Assessment and Pumping Test Design for Groundwater Prediction Using Decision Theory and Genetic Algorithms
AU: * McPhee, J
EM: jmcphee@ing.uchile.cl
AF: University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095 United States
AU: * McPhee, J
EM: jmcphee@ing.uchile.cl
AF: Universidad de Chile, Av. Blanco Encalada 2002, Santiago, 837-0449 Chile
AU: William, Y W
EM: williamy@seas.ucla.edu
AF: University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095 United States
AB: This work presents a methodology for pumping test design based on the reliability requirements of a groundwater model. Reliability requirements take into consideration the application of the model results in groundwater management, expressed in this case as a multiobjective management model. The pumping test design is formulated as a mixed-integer nonlinear programming (MINLP) problem and solved using a combination of genetic algorithm (GA) and gradient-based optimization. Bayesian decision theory provides a formal framework for assessing the influence of parameter uncertainty over the reliability of the proposed pumping test. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential 'true' states of the system
DE: 1805 Computational hydrology
DE: 1829 Groundwater hydrology
DE: 1847 Modeling
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005