Improving the efficiency of Monte Carlo Bayesian calibration of hydrologic models via model pre-emption
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Date
2015-09-01
Authors
Shafii, Mahyar
Tolson, Bryan A.
Matott, Loren Shawn
Journal Title
Journal ISSN
Volume Title
Publisher
International Water Association
Abstract
Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling and sequential Monte Carlo (SMC) sampling are popular methods for uncertainty analysis in hydrological modelling. However, application of these methodologies can incur significant computational costs. This study investigated using model pre-emption for improving the computational efficiency of MCMC and SMC samplers in the context of hydrological modelling. The proposed pre-emption strategy facilitates early termination of low-likelihood simulations and results in reduction of unnecessary simulation time steps. The proposed approach is incorporated into two samplers and applied to the calibration of three rainfall-runoff models. Results show that overall pre-emption savings range from 5 to 21%. Furthermore, results indicate that pre-emption savings are greatest during the pre-convergence 'burn-in' period (i.e., between 8 and 39%) and decrease as the algorithms converge towards high likelihood regions of parameter space. The observed savings are achieved with absolutely no change in the posterior set of parameters.
Description
Final published version available at: Shafii, M., Tolson, B., & Shawn Matott, L. (2015). Improving the efficiency of Monte Carlo Bayesian calibration of hydrologic models via model pre-emption. Journal of Hydroinformatics, 17(5), 763–770. https://doi.org/10.2166/hydro.2015.043
Keywords
Ar, Calibration, Dream, Pre-Emption, Smc, Uncertainty Analysis