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A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling
Author(s) -
Camacho René A.,
Martin James L.,
McAnally William,
DíazRamirez Jairo,
Rodriguez Hugo,
Sucsy Peter,
Zhang Song
Publication year - 2015
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12319
Subject(s) - markov chain monte carlo , glue , bayesian probability , prior probability , parametric statistics , monte carlo method , uncertainty quantification , mathematics , likelihood function , parameter space , statistics , bayesian inference , estimation theory , computer science , mathematical optimization , engineering , mechanical engineering
We evaluate and compare the performance of Bayesian Monte Carlo ( BMC ), Markov chain Monte Carlo ( MCMC ), and the Generalized Likelihood Uncertainty Estimation ( GLUE ) for uncertainty analysis in hydraulic and hydrodynamic modeling ( HHM ) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC , our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.