dc.contributor.authorYu, J. J.
dc.contributor.authorQin, Xiaosheng
dc.contributor.authorLarsen, O.
dc.date.accessioned2014-09-22T07:36:15Z
dc.date.available2014-09-22T07:36:15Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.citationYu, 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.en_US
dc.identifier.issn0885-6087en_US
dc.identifier.urihttp://hdl.handle.net/10220/20943
dc.description.abstractA 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.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent39 p. + 6 p. figuresen_US
dc.language.isoenen_US
dc.relation.ispartofseriesHydrological processesen_US
dc.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].en_US
dc.subjectDRNTU::Engineering::Civil engineering::Water resources
dc.titleUncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic samplingen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1002/hyp.10249
dc.description.versionAccepted versionen_US


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