Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami

Handle URI:
http://hdl.handle.net/10754/563824
Title:
Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami
Authors:
Sraj, Ihab ( 0000-0002-6158-472X ) ; Mandli, Kyle T.; Knio, Omar; Dawson, Clint N.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning's n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning's n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Environmental Science and Engineering Program; Earth Science and Engineering Program; Applied Mathematics and Computational Science Program; Clean Combustion Research Center; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Ocean Modelling
Issue Date:
Nov-2014
DOI:
10.1016/j.ocemod.2014.09.001
Type:
Article
ISSN:
14635003
Sponsors:
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Clean Combustion Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSraj, Ihaben
dc.contributor.authorMandli, Kyle T.en
dc.contributor.authorKnio, Omaren
dc.contributor.authorDawson, Clint N.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T12:15:42Zen
dc.date.available2015-08-03T12:15:42Zen
dc.date.issued2014-11en
dc.identifier.issn14635003en
dc.identifier.doi10.1016/j.ocemod.2014.09.001en
dc.identifier.urihttp://hdl.handle.net/10754/563824en
dc.description.abstractTsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning's n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning's n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.en
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).en
dc.publisherElsevier BVen
dc.subjectBayesian inferenceen
dc.subjectManning's n friction coefficienten
dc.subjectPolynomial Chaosen
dc.subjectSensitivity analysisen
dc.subjectTsunamien
dc.titleUncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunamien
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentClean Combustion Research Centeren
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalOcean Modellingen
dc.contributor.institutionInstitute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th ST. Stop C0200Austin, TX, United Statesen
dc.contributor.institutionDepartment of Mechanical Engineering and Materials Science, Duke University, 144 Hudson HallDurham, NC, United Statesen
kaust.authorSraj, Ihaben
kaust.authorKnio, Omaren
kaust.authorHoteit, Ibrahimen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.