Uncertainty quantification of landslide generated waves using gaussian process emulation and variance-based sensitivity analysis

Snelling, Branwen; Neethling, Stephen; Horsburgh, Kevin ORCID: https://orcid.org/0000-0003-4803-9919; Collins, Gareth; Piggott, Matthew. 2020 Uncertainty quantification of landslide generated waves using gaussian process emulation and variance-based sensitivity analysis. Water, 12 (2). 416. https://doi.org/10.3390/w12020416

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Official URL: http://dx.doi.org/10.3390/w12020416

Abstract/Summary

Simulations of landslide generated waves (LGWs) are prone to high levels of uncertainty. Here we present a probabilistic sensitivity analysis of an LGW model. The LGW model was realised through a smooth particle hydrodynamics (SPH) simulator, which is capable of modelling fluids with complex rheologies and includes flexible boundary conditions. This LGW model has parameters defining the landslide, including its rheology, that contribute to uncertainty in the simulated wave characteristics. Given the computational expense of this simulator, we made use of the extensive uncertainty quantification functionality of the Dakota toolkit to train a Gaussian process emulator (GPE) using a dataset derived from SPH simulations. Using the emulator we conducted a variance-based decomposition to quantify how much each input parameter to the SPH simulation contributed to the uncertainty in the simulated wave characteristics. Our results indicate that the landslide’s volume and initial submergence depth contribute the most to uncertainty in the wave characteristics, while the landslide rheological parameters have a much smaller influence. When estimated run-up is used as the indicator for LGW hazard, the slope angle of the shore being inundated is shown to be an additional influential parameter. This study facilitates probabilistic hazard analysis of LGWs, because it reveals which source characteristics contribute most to uncertainty in terms of how hazardous a wave will be, thereby allowing computational resources to be focused on better understanding that uncertainty.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/w12020416
ISSN:	2073-4441
Date made live:	06 May 2020 13:05 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/527644

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3390/w12020416

ISSN:

2073-4441

Date made live:

06 May 2020 13:05 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/527644