Bayesian model averaging on hydraulic conductivity estimation and groundwater head prediction
type: CreativeWork
description: Characterization of aquifer heterogeneity is inherently difficult because of the insufficiency of data, the inflexibility of parameterization methods, and non-uniqueness of parameterization methods. Groundwater predictions are greatly affected by multiple interpretations of aquifer properties and the uncertainties of model parameters. This study introduces a Bayesian model averaging (BMA) method along with multiple generalized parameterization (GP) methods to identify hydraulic conductivity and along with multiple simulation models to predict groundwater head and quantify the prediction uncertainty. Two major issues about BMA are discussed. The first problem is with using Occam’s window in usual BMA applications. Occam’s window only accepts models in a very narrow range, tending to single out the best method and discard other good methods. A variance window is proposed to replace Occam’s window to cope with this problem. The second problem is with using the Kashyap information criterion (KIC) in the approximation of posterior model probabilities, which tends to prefer highly uncertain model by considering the Fisher information matrix. The Bayesian information criterion (BIC) is recommended because it is able to avoid controversial results and it is computationally efficient. Numerical examples are designed to test the Bayesian model averaging method on hydraulic conductivity identification and groundwater head prediction. The proposed methodologies are then applied to the hydraulic conductivity identification of the Alamitos Gap area, and the hydraulic conductivity estimation and groundwater head prediction of the “1,500-foot” sand in East Baton Rouge Parish, Louisiana. The results show that the GP method provides great flexibility in parameterization with small conditional variance. The use of the variance window is necessary to avoid a dominant model when many models perform equally well. Compared to KIC, BIC is able to give an unbiased posterior model probabilit…
abstract: Characterization of aquifer heterogeneity is inherently difficult because of the insufficiency of data, the inflexibility of parameterization methods, and non-uniqueness of parameterization methods. Groundwater predictions are greatly affected by multiple interpretations of aquifer properties and the uncertainties of model parameters. This study introduces a Bayesian model averaging (BMA) method along with multiple generalized parameterization (GP) methods to identify hydraulic conductivity and along with multiple simulation models to predict groundwater head and quantify the prediction uncertainty. Two major issues about BMA are discussed. The first problem is with using Occam’s window in usual BMA applications. Occam’s window only accepts models in a very narrow range, tending to single out the best method and discard other good methods. A variance window is proposed to replace Occam’s window to cope with this problem. The second problem is with using the Kashyap information criterion (KIC) in the approximation of posterior model probabilities, which tends to prefer highly uncertain model by considering the Fisher information matrix. The Bayesian information criterion (BIC) is recommended because it is able to avoid controversial results and it is computationally efficient. Numerical examples are designed to test the Bayesian model averaging method on hydraulic conductivity identification and groundwater head prediction. The proposed methodologies are then applied to the hydraulic conductivity identification of the Alamitos Gap area, and the hydraulic conductivity estimation and groundwater head prediction of the “1,500-foot” sand in East Baton Rouge Parish, Louisiana. The results show that the GP method provides great flexibility in parameterization with small conditional variance. The use of the variance window is necessary to avoid a dominant model when many models perform equally well. Compared to KIC, BIC is able to give an unbiased posterior model probabilit…
author: Li, Xiaobao
keywords: Groundwater Modeling, KIC, BIC, Bayesian Model Averaging
date published: 2008-01-01T08:00:00Z
date modified: 2026-07-24T02:57:56Z
provider: Lousiana State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Civil and Environmental Engineering

PropertyValue
property ID: DOI
value: 10.31390/gradschool_dissertations.609