A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques

Journal of Marine Science and Engineering

卷:9

Alexandre Thibault¹  Axel Aurouet¹  Rodrigo Pedreros²  Déborah Idier²  Audrey Baills²  Jérémy Rohmer²  Thierry Klein³  François Bachoc³  José Betancourt³  Fabrice Gamboa³  Andrés F. López-Lopera³ 

[1] Antea Group, 45160 Olivet, France;

[2] BRGM, 45060 Orléans, France;

[3] Institut de Mathématiques de Toulouse (IMT), 31400 Toulouse, France;

关键词: forecast;    flood;    local;    hydrodynamic modelling;    metamodelling;    Gâvres;   

DOI  :  10.3390/jmse9111191

来源: DOAJ

【 摘 要 】

Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., mathematical functions that precisely and efficiently (within minutes) estimate the results that would provide the numerical model. While the principle of relying on metamodel solutions is not new, the originality of the present work is to tackle and validate the entire process from the identification of user needs to the establishment and validation of the rapid forecast and early warning system (FEWS) while relying on numerical modelling, metamodelling, the development of indicators, and information technologies. The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process- and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.

【 授权许可】

Unknown