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A predictive data-driven approach based on reduced order models for the morphodynamic study of a coastal water intake

Mouradi, Rem-Sophia and Goeury, Cédric and Thual, Olivier and Zaoui, Fabrice and Tassi, Pablo A predictive data-driven approach based on reduced order models for the morphodynamic study of a coastal water intake. (2020) In: Advances in Hydroinformatics : SimHydro 2019 : Models for extreme situations and crisis management. (Springer Water). Springer, Singapore, 849-866. ISBN 978-981-15-5436-0

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Official URL: https://doi.org/10.1007/978-981-15-5436-0_66

Abstract

For many environmental applications, field measurement techniques are increasingly evolving, resulting in more complex and complete datasets. The statistical analysis of these datasets is challenging, and requires the use of relevant mathematical tools. Furthermore, the access to a richer collection of data offers a new optimistic perspective on data-driven modeling, to complement, or even replace, process-based modeling. The presented work is within the context of a power plant water intake monitoring. The intake channel is subject to massive sediment arrivals, which represents a clogging risk. One of the challenges is therefore to better understand the sediment dynamics observed in the channel, and to characterize their correlation to environmental forcing. The final goal is to proceed to the forecasting of the dynamics using the knowledge of forcing parameters. Luckily, due to monitoring needs, bathymetric measurements of the channel are realized on a regular basis, along with meteorological and hydrodynamic survey. A statistical study is hereby proposed on the basis of this data. Firstly, a Proper Orthogonal Decomposition (POD) is applied to the two-dimensional bathymetric data set, in order to reduce it to a low-dimensional set of time dependent scalar coefficients. The latter are linked to the physical forcings via an adapted statistical model. In this study, a Polynomial Chaos Expansion (PCE) is used for this purpose. Consequently, a data-driven model is proposed, on the basis of a POD-PCE coupling. The proposed step-by-step methodology could also be transposed to other applications.

Item Type:Book Section
Additional Information:Mouradi RS., Goeury C., Thual O., Zaoui F., Tassi P. (2020) A Predictive Data-Driven Approach Based on Reduced Order Models for the Morphodynamic Study of a Coastal Water Intake. In: Gourbesville P., Caignaert G. (eds) Advances in Hydroinformatics. Springer Water. Springer, Singapore. https://doi.org/10.1007/978-981-15-5436-0_66 Springer water ISSN 2364-6934
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > EDF (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Other partners > Centre Européen de Recherche et Formation Avancées en Calcul Scientifique - CERFACS (FRANCE)
Laboratory name:
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Deposited On:03 May 2021 10:56

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