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Multifidelity computer model emulation with high‐dimensional output: An application to storm surge

Ma, P. and Karagiannis, G. and Konomi, B. A. and Asher, T. G. and Toro, G. R. and Cox, A. T. (2022) 'Multifidelity computer model emulation with high‐dimensional output: An application to storm surge.', Journal of the Royal Statistical Society: Series C (Applied Statistics) .

Abstract

Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.

Item Type:Article
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Available under License - Creative Commons Attribution Non-commercial 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1111/rssc.12558
Publisher statement:© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Date accepted:19 February 2022
Date deposited:19 April 2022
Date of first online publication:09 April 2022
Date first made open access:19 April 2022

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