Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high-fidelity models being more expensive than low-fidelity ones. However, when assessing uncertainty, we can use a multifidelity approach to take advantage of the accuracy of high-fidelity models and the computational efficiency of low-fidelity models. Here, we apply the multilevel multifidelity Monte Carlo method (MLMF) to quantify uncertainty by computing statistical estimators of key output variables with respect to uncertain input data, using the high-fidelity hydrodynamic model XBeach and the lower-fidelity coastal flooding model SFINCS (Super-Fast INundation of CoastS). The multilevel aspect opens up the further advantageous possibility of applying each of these models at multiple resolutions. This work represents the first application of MLMF in the coastal zone and one of its first applications in any field. For both idealised and real-world test cases, MLMF can significantly reduce computational cost for the same accuracy compared to both the standard Monte Carlo method and to a multilevel approach utilising only a single model (the multilevel Monte Carlo method). In particular, here we demonstrate using the case of Myrtle Beach, South Carolina, USA, that this improvement in computational efficiency allows for in-depth uncertainty analysis to be conducted in the case of real-world coastal environments – a task that would previously have been practically unfeasible. Moreover, for the first time, we show how an inverse transform sampling technique can be used to accurately estimate the cumulative distribution function (CDF) of variables from the MLMF outputs. MLMF-based estimates of the expectations and the CDFs of the variables of interest are of significant value to decision makers when assessing uncertainty in predictions.
Date Issued
2022-08-03
Date Acceptance
2022-07-11
Citation
Natural Hazards and Earth System Sciences, 2022, 22 (8), pp.2491-2515
ISSN
1561-8633
Publisher
Copernicus Publications
Start Page
2491
End Page
2515
Journal / Book Title
Natural Hazards and Earth System Sciences
Volume
22
Issue
8
Copyright Statement
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
License URL
Sponsor
Engineering and Physical Sciences Research Council
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://nhess.copernicus.org/articles/22/2491/2022/
Grant Number
EP/R512540/1
EP/L000407/1
Subjects
Strategic, Defence & Security Studies
0403 Geology
0406 Physical Geography and Environmental Geoscience
0911 Maritime Engineering
Publication Status
Published
Date Publish Online
2022-08-03