Inversion Algorithm for Civil Flood Defense Optimisation: Application to Two-Dimensional Numerical Model of the Garonne River in France
- 1Institut de Radioprotection et de Sûreté Nucléaire, France
The objective of this study is to investigate the "inversion-approach" for the optimization of flood defense in a inundated area. This is a new methodology in this engineering field which consists in the definition of a "safety criterion" (as for instance "the water level in a given location must be lower than a given value") and the analysis of all the uncertain controlled parameters (i.e. flood defense geometry, location, and so on) combinations allowing to not exceed the safety objective for all the possible uncontrolled parameters combination (i.e. the flow hydrograph parameters) representing the natural phenomenon. In order to estimate this safety set, we will use a transient meta-modelling approach, which reduces widely the number of model evaluations required. This algorithm relies on a kriging surrogate built from few model evaluations, sequentially fulfilled with new numerical model evaluations as long as the remaining uncertainty of the whole safety set remains too high. Also known as "Stepwise Uncertainty Reduction", this algorithm is embedded in the Funz engine in charge of bridging the numerical model and any design of experiments algorithm.
We applied this algorithm to a real bi-dimensional numerical model of the Garonne River. Especially, we focused our attention on the water depth at a given area when considering the influence of a simplified flood defense during a flooding event. In order to simplify the problem, we only analyze the two parameters describing the slab and dyke elevations of the flood defense system. The results will be analyzed in terms of safety control inside the operating range of the river. It appears that once properly evaluated, this constrained zone gives highly valuable data for a fully risk informed management of the area to protect.
Keywords: Kriging, Surrogate accelerated model, Telemac, Bayesian optimisation, inversion, level set, Garonne, Hydrogram, uncertainty
Received: 18 Dec 2018;
Accepted: 30 Sep 2019.
Copyright: © 2019 Richet and Bacchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Yann Richet, Institut de Radioprotection et de Sûreté Nucléaire, Fontenay-aux-Roses, France, firstname.lastname@example.org