HR: 16:30h
AN: H14A-03 INVITED     [Abstracts]
TI: Multi-model Ranking And Inference In Ground-Water Modeling
AU: * Poeter, E P
EM: epoeter@mines.edu
AF: International Ground Water Modeling Center, Colorado School of Mines, Dept of GE 1500 Illinois St., Golden, CO 80401 United States
AU: Anderson, D R
EM: aicanderson1@comcast.net
AF: Applied Information Company, 707 Breakwater Drive, Fort Collins, CO 80525 United States
AB: Uncertainty of hydrogeologic conditions makes it important to evaluate alternative plausible models in an effort to evaluate the character of a groundwater system, maintain parsimony, and make predictions with reasonable definition of their uncertainty. When multiple models are considered, data collection and analysis focuses on evaluation of which model(s) is(are) most supported by the data. Generally more than one model provides a similar acceptable fit to the observations, thus inference should be made from multiple models. Kullback-Leibler (K-L) information provides a rigorous foundation for model inference. Model evaluation based on K-L information is simple to compute, easy to interpret, and yields parsimonious models with more realistic measures of precision than evaluation of any one model, or evaluation based on other commonly referenced model selection criteria. Akaike's AICc, as modified by others after him, is a good tool for estimating K-L information. Alternative criteria such as: Bayesian information criterion (BIC of Schwarz), Hannan and Quinn's criterion (HQ), and Kashyap's criterion (KIC) have been suggested for selection of ground water models. Although use of these criteria for model ranking and multi-model inference produces similar results in practice: they strive to identify the best model; are based on the notion that full reality can be represented by a model exactly; and finally, assume that such a model is in the set of candidate models. This is in sharp contrast to the information-theoretic approach based on AICc where models are considered to be approximations to reality. In addition, as the number of observations increase, more details of the system can be uncovered, thus AICc selects more complex models. In contrast, BIC, HQ, and KIC seek the true model with consistent complexity regardless of the available number of observations. A computer-generated example illustrates the method.
DE: 1829 Groundwater hydrology
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting