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Uncertainty Quantification in 2D Morphodynamic Model: Application to the Gironde Estuary.

8 pagesPublished: September 20, 2018

Abstract

The purpose of uncertainty propagation is the quantification of input data uncertainties on the output results. This involves understanding (i) how uncertainty is represented in the model structure and the input data ? (ii) how are uncertainties propagated in the model ? (iii) Which uncertainties affect mostly the model outputs ? The propagation analysis be- gins with the identification and characterization of the uncertainties of the input data. The aim of this work is to estimate the uncertainties pertaining the parameters of a 2D morphodynamic model so as to characterize the probability distribution P h(x, y, t) ≤ hcritical of the water depth h(x, y, t) over the Gironde Estuary, where hcritical is a critical threshold of the water depth h(x,y,t) that allows navigation. To handle this purpose, we propose an original approach that includes sediment parameters and bathymetry data, through the use of probabilistic methods, imprecise probability and non-linear regression. The pro- posed strategy offers flexibility to handle the variability of these data are also suitable for data-driven applications since the uncertainty quantification can also be conducted from a small set of parameters of the 2D morphodynamic model.

Keyphrases: confidence interval, maximum likelihood, Monte Carlo method

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1147--1154

Links:
BibTeX entry
@inproceedings{HIC2018:Uncertainty_Quantification_in_2D,
  author    = {Romain Leroux and C\textbackslash{}'edric Goeury and Kamal El Kadi Abderrezzak and Pablo Tassi},
  title     = {Uncertainty Quantification in 2D Morphodynamic Model: Application to the Gironde Estuary.},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {1147--1154},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/1B9j},
  doi       = {10.29007/6n1t}}
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