Imperial College London
Alternate

ProfessorMatthewPiggott

Faculty of Engineering, Department of Earth Science & Engineering

Professor of Computational Geoscience and Engineering
 
 
 
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Contact

 

m.d.piggott Website

 
 
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Location

 

4.82Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Clare:2022:10.5194/nhess-22-2491-2022,
author = {Clare, MCA and Leijnse, TWB and McCall, RT and Diermanse, FLM and Cotter, CJ and Piggott, MD},
doi = {10.5194/nhess-22-2491-2022},
journal = {Natural Hazards and Earth System Sciences},
pages = {2491--2515},
title = {Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding},
url = {http://dx.doi.org/10.5194/nhess-22-2491-2022},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Clare,MCA
AU  - Leijnse,TWB
AU  - McCall,RT
AU  - Diermanse,FLM
AU  - Cotter,CJ
AU  - Piggott,MD
DO  - 10.5194/nhess-22-2491-2022
EP  - 2515
PY  - 2022///
SN  - 1561-8633
SP  - 2491
TI  - Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding
T2  - Natural Hazards and Earth System Sciences
UR  - http://dx.doi.org/10.5194/nhess-22-2491-2022
UR  - https://nhess.copernicus.org/articles/22/2491/2022/
UR  - http://hdl.handle.net/10044/1/99174
VL  - 22
ER  -
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