Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami
Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami
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DOI：
10.1016/j.ocemod.2014.09.001
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发表时间：
2014-11
期刊：
影响因子：
3.2
通讯作者：
I. Sraj;K. Mandli;O. Knio;C. Dawson;I. Hoteit
中科院分区：
文献类型：
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作者：
I. Sraj;K. Mandli;O. Knio;C. Dawson;I. Hoteit
海啸计算模型用于探索多种洪水情景和预测水位。然而，准确估计水位高度需要准确估计许多模型参数，包括曼宁非摩擦参数化。我们的目标是为曼宁系数的不确定性量化和推断开发一种有效的方法，我们在这里通过三个不同的参数来描述曼宁系数，这些参数在岸上、近岸和深水区域设置为常数，使用等浴场定义。我们使用多项式混沌（PC）为geoclaw模型建立了一个廉价的代理模型，并使用贝叶斯推理来估计和量化使用Tōhoku海啸期间收集的DART浮标数据的相关参数的不确定性。代理模型显著降低了贝叶斯推理的马尔可夫链蒙特卡罗（MCMC）抽样的计算负担。PC代理也用于执行敏感性分析。
Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning’snfriction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning’sncoefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the GeoClawmodel and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.