Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis
Uncertainty Quantification of Landslide Generated Waves Using Gaussian Process Emulation and Variance-Based Sensitivity Analysis
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DOI：
10.3390/w12020416
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发表时间：
2020-02-01
期刊：
影响因子：
3.4
通讯作者：
Piggott, Matthew
中科院分区：
文献类型：
--
作者：
Snelling, Branwen;Neethling, Stephen;Piggott, Matthew
滑坡波的模拟具有很高的不确定性。在这里，我们提出了一个LGW模型的概率灵敏度分析。LGW模型是通过光滑粒子流体动力学（SPH）模拟器实现的，该模拟器能够模拟具有复杂流变性的流体，并包括灵活的边界条件。该LGW模型具有定义滑坡的参数，包括其流变学，这些参数导致模拟波特性的不确定性。考虑到该模拟器的计算费用，我们利用Dakota工具包的广泛的不确定性量化功能，使用SPH模拟的数据集训练高斯过程模拟器（GPE）。使用仿真器，我们进行了基于方差的分解，以量化SPH模拟的每个输入参数对模拟波浪特性的不确定性的贡献。研究结果表明，滑坡体的体积和初始淹没深度对波浪特性的不确定性贡献最大，而滑坡体流变参数的影响要小得多。当估计的助跑被用作指标的LGW的危险，被淹没的海岸的倾斜角被证明是一个额外的影响参数。这项研究有利于概率危险分析的LGWs，因为它揭示了哪些源特性贡献最大的不确定性，如何危险的波将是，从而使计算资源集中在更好地理解这种不确定性。
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.