Next Article in Journal
Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data
Previous Article in Journal
Trend Analyses of Meteorological Variables and Lake Levels for Two Shallow Lakes in Central Turkey
Open AccessArticle

Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis

1
Department of Earth Science and Engineering, South Kensington Campus, Imperial College, London SW7 2BP, UK
2
National Oceanography Centre, Joseph Proudman Building 6 Brownlow Street, Liverpool L3 5DA, UK
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 4th IMA International Conference on Flood Risk.
Water 2020, 12(2), 416; https://doi.org/10.3390/w12020416
Received: 8 December 2019 / Revised: 17 January 2020 / Accepted: 24 January 2020 / Published: 4 February 2020
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: submarine landslide; waves; uncertainty quantification; Gaussian process emulation; variance-based sensitivity; smooth particle hydrodynamics submarine landslide; waves; uncertainty quantification; Gaussian process emulation; variance-based sensitivity; smooth particle hydrodynamics
Show Figures

Figure 1

MDPI and ACS Style

Snelling, B.; Neethling, S.; Horsburgh, K.; Collins, G.; Piggott, M. Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis. Water 2020, 12, 416.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop