Runup and uncertainty quantification: sensitivity analysis via ANOVA decomposition

We investigate the ability of uncertainty quantification techniques to act as enablers for the study of the sensitivity of dynamics of dam breaks to the variations of model parameters. In particular, we make use of sensitivity indexes computed by means of an Analysis of Variance (ANOVA) to provide the sensitivity of the runup dynamics to the variations of parameters such wave amplitude, friction coefficient, etc. The sensitivity indexes, known as Sobol indexes, are obtained following (Crestaux-LeMaitre-Martinez, 2009) by resorting to a non-intrusive polynomial chaos method allowing to reconstruct a complete representation of the variation of the outputs in the parameter space, and to compute the sensitivity indexes via the ANOVA decomposition. To increase the reliability of the results, we perfom the study independently with two models based on a discretization of the shallow water equations, developed in (Ricchiuto, 2014), and (Nikolos and Delis, 2009), respectively. The approach proposed provides simultaneously the variance of the outputs and their sensitivity to each independent parameter, allowing to construct a hierarchy of parameters which depends on the flow conditions.

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Additional Info

Field Value
Source https://inria.hal.science/hal-00985390
Author Ricchiuto, Mario, Congedo, Pietro Marco, Delis, Argiris I.
Maintainer CCSD
Last Updated May 5, 2026, 12:36 (UTC)
Created May 5, 2026, 12:36 (UTC)
Identifier Report N°: RR-8530
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Parallel tools for Numerical Algorithms and Resolution of essentially Hyperbolic problems (BACCHUS) ; Centre Inria de l'Université de Bordeaux ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)
creator Ricchiuto, Mario
date 2014-04-29T00:00:00
harvest_object_id 97b682ab-64fc-4cae-bee1-7d3a9f084a4d
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-03-18T00:00:00
set_spec type:REPORT