A methodology for deriving extreme nearshore sea conditions for structural design and flood risk analysis
A methodology for deriving extreme nearshore sea conditions for structural design and flood risk analysis
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DOI：
10.1016/j.coastaleng.2014.01.012
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发表时间：
2014-06-01
影响因子：
4.4
通讯作者：
Minguez, R.
中科院分区：
文献类型：
--
作者：
Gouldby, B.;Mendez, F. J.;Minguez, R.
沿海洪水风险分析和结构设计需要近岸地区的极端海况。过去应用的许多多元极值方法受到与极值的依赖结构有关的假设的限制。条件极值统计模型克服了以前的一些限制。为了在实践中应用该方法，需要一个蒙特卡罗采样过程，即模拟合成生成事件的大样本。蒙特卡罗方法的使用，结合计算密集型的物理过程模型，可以在计算方面提出重大的实际挑战。为了克服这些挑战，人们对元模型的使用进行了广泛的研究。元模型是计算密集型物理过程模型（模拟器）的近似值。它们是通过将函数拟合到模拟器的输出而得到的。由于它们的简化表示，它们在计算上比它们近似的模拟器更有效。在这里，描述了一种方法，用于导出一个大的蒙特卡罗样本的极端近岸海况。该方法包括使用条件极值模型生成大量近海海洋条件样本。然后建立了波变换过程的元模型。使用聚类算法来帮助元模型的开发。然后使用元模型将大量离岸数据样本转换为近岸数据。所得的近岸海况可用于建筑物的概率设计或洪水风险分析。本文描述了该方法在西班牙北海岸的一个案例研究地点的应用。(C) 2014 Elsevier B.V.版权所有
Extreme sea conditions in the nearshore zone are required for coastal flood risk analysis and structural design. Many multivariate extreme value methods that have been applied in the past have been limited by assumptions relating to the dependence structure in the extremes. A conditional extremes statistical model overcomes a number of these previous limitations. To apply the method in practice, a Monte Carlo sampling procedure is required whereby large samples of synthetically generated events are simulated. The use of Monte Carlo approaches, in combination with computationally intensive physical process models, can raise significant practical challenges in terms of computation. To overcome these challenges there has been extensive research into the use of meta-models. Meta-models are approximations of computationally intensive physical process models (simulators). They are derived by fitting functions to the outputs from simulators. Due to their simplified representation they are computationally more efficient than the simulators they approximate. Here, a methodology for deriving a large Monte Carlo sample of extreme nearshore sea states is described. The methodology comprises the generation of a large sample of offshore sea conditions using the conditional extremes model. A meta-model of the wave transformation process is then constructed. A clustering algorithm is used to aid the development of the meta-model. The large sample of offshore data is then transformed through to the nearshore using the meta-model. The resulting nearshore sea states can be used for the probabilistic design of structures or flood risk analysis. The application of the methodology to a case study site on the North Coast of Spain is described. (C) 2014 Elsevier B.V. All rights reserved.