HR: 09:30h
AN: H21K-06 [Abstracts]
TI: Parameter Estimation and Parameterization Uncertainty Using Bayesian Model Averaging
AU: * Tsai, F T
EM: ftsai@lsu.edu
AF: Louisiana State University,
Department of Civil and Environmental Engineering, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803,
AU: Li, X
EM: xli11@lsu.edu
AF: Louisiana State University,
Department of Civil and Environmental Engineering, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803,
AB:
This study proposes Bayesian model averaging (BMA) to address parameter estimation uncertainty arisen from
non-uniqueness in parameterization methods. BMA provides a means of incorporating multiple parameterization
methods for prediction through the law of total probability, with which an ensemble average of hydraulic
conductivity distribution is obtained. Estimation uncertainty is described by the BMA variances, which contain
variances within and between parameterization methods. BMA shows the facts that considering more
parameterization methods tends to increase estimation uncertainty and estimation uncertainty is always
underestimated using a single parameterization method. Two major problems in applying BMA to hydraulic
conductivity estimation using a groundwater inverse method will be discussed in the study. The first problem is
the use of posterior probabilities in BMA, which tends to single out one best method and discard other good
methods. This problem arises from Occam's window that only accepts models in a very narrow range. We
propose a variance window to replace Occam's window to cope with this problem. The second problem is the
use of Kashyap information criterion (KIC), which makes BMA tend to prefer high uncertain parameterization
methods due to considering the Fisher information matrix. We found that Bayesian information criterion (BIC) is a
good approximation to KIC and is able to avoid controversial results. We applied BMA to hydraulic conductivity
estimation in the 1,500-foot sand aquifer in East Baton Rouge Parish, Louisiana.
DE: 1829 Groundwater hydrology
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting