Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79427
Title: Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling
Authors: Yu, J. J.
Qin, Xiaosheng
Larsen, O.
Keywords: DRNTU::Engineering::Civil engineering::Water resources
Issue Date: 2014
Source: Yu, J. J., Qin, X. S., & Larsen, O. (2014). Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling. Hydrological processes, in press.
Series/Report no.: Hydrological processes
Abstract: A generalized likelihood uncertainty estimation (GLUE) method incorporating moving least squares (MLS) with entropy for stochastic sampling (denoted as GLUE-MLS-E) was proposed for uncertainty analysis of flood inundation modelling. The MLS with entropy (MLS-E) was established according to the pairs of parameters/likelihoods generated from a limited number of direct model executions. It was then applied to approximate the model evaluation to facilitate the target sample acceptance of GLUE during the Monte-Carlo-based stochastic simulation process. The results from a case study showed that the proposed GLUE-MLS-E method had a comparable performance as GLUE in terms of posterior parameter estimation and predicted confidence intervals; however, it could significantly reduce the computational cost. A comparison to other surrogate models, including MLS, quadratic response surface and artificial neural networks (ANN), revealed that the MLS-E outperformed others in light of both the predicted confidence interval and the most likely value of water depths. ANN was shown to be a viable alternative, which performed slightly poorer than MLS-E. The proposed surrogate method in stochastic sampling is of practical significance in computationally expensive problems like flood risk analysis, real-time forecasting, and simulation-based engineering design, and has a general applicability in many other numerical simulation fields that requires extensive efforts in uncertainty assessment.
URI: https://hdl.handle.net/10356/79427
http://hdl.handle.net/10220/20943
ISSN: 0885-6087
DOI: 10.1002/hyp.10249
Rights: © 2014 John Wiley & Sons, Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Hydrological Processes. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1002/hyp.10249].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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