The use of surrogate modeling techniques for storm surge estimation is providing unique opportunities for coastal hazard analysis and risk assessment. Specifically, surrogate models can support a comprehensive estimation of the coastal hazard/risk utilizing ensembles with large number of storm simulations. A critical challenge in this assessment is the high-dimensionality of the output, which needs to be handled efficiently both in terms of computational burden and, especially in the context of automated risk assessment tools, of memory requirements. Though dimensionality reduction techniques, like Principal Component Analysis (PCA), can address this challenge for the surrogate model calibration, the same does not apply for the hazard/risk estimation performed using the trained surrogate model. In this paper, the estimation of coastal risk using large storm ensemble predictions is examined for domains with hundreds of thousands of point locations (grid nodes of the storm surge computational model) for which the corresponding hazard curves need to be provided. This is achieved by exploiting their geospatial distribution and their surge response correlations within the high dimensional original output. K-means clustering is adopted to identify a small subset of nodes to serve as basis for the hazard estimation: instead of calculating the response for the storm ensemble over the entire grid, and then calculating the hazard curves, the hazard curves are first produced for this small subset, and then interpolated over the original grid. Kriging is examined for the latter geospatial interpolation, and its formulation is enhanced to support the desired application, with modifications established for both the calibration stage, as well as the efficient implementation of the interpolation. Additionally, different variants are examined for the clustering stage using information from: (i) the spatial structure of the grid, (ii) the characteristics of the response variability across the geographical domain, and (iii) the combination of the above two. A comprehensive framework is presented integrating both the surrogate model development and the hurricane hazard estimation, with the computational benefits offered in the latter estimation by the use of a small subset of nodes discussed in detail. A validation study is performed utilizing the Coastal Hazards System's (CHS) North Atlantic Coast Comprehensive Study (NACCS) database. It is shown that the achieved numerical efficiency is significant, offering a 50–250-fold reduction of the computational burden with only a moderate impact on the accuracy of the estimated hazard curves.

使用替代建模技术进行风暴潮估计为沿海灾害分析和风险评估提供了独特的机会。具体来说，替代模型可以使用具有大量风暴模拟的集合来支持对沿海灾害/风险的综合估计。此评估中的一个关键挑战是输出的高维性，需要在计算负担方面进行有效处理，尤其是在自动风险评估工具的背景下，内存要求。尽管诸如主成分分析 (PCA) 之类的降维技术可以解决替代模型校准的这一挑战，但同样不适用于使用经过训练的替代模型执行的危险/风险估计。在本文中，针对需要提供相应危害曲线的具有数十万个点位置（风暴潮计算模型的网格节点）的域，检查了使用大型风暴集合预测对沿海风险的估计。这是通过在高维原始输出中利用它们的地理空间分布和它们的浪涌响应相关性来实现的。钾-means聚类被用来识别一个小的节点子集作为风险估计的基础：而不是计算整个网格上风暴集合的响应，然后计算风险曲线，首先为此生成风险曲线小子集，然后在原始网格上进行插值。克里金法针对后者的地理空间插值进行了检查，其公式得到增强以支持所需的应用程序，并为校准阶段以及插值的有效实施进行了修改。此外，使用以下信息检查聚类阶段的不同变体：(i) 网格的空间结构，(ii) 跨地理域响应可变性的特征，以及 (iii) 以上两者的组合。提出了一个综合框架，集成了替代模型开发和飓风灾害估计，后者通过使用一个小的节点子集进行了详细讨论，从而在后者的估计中提供了计算优势。验证研究是利用沿海灾害系统 (CHS) 的北大西洋海岸综合研究 (NACCS) 数据库进行的。结果表明，实现的数值效率是显着的，将计算负担减少了 50-250 倍，而对估计的危险曲线的准确性只有中等影响。验证研究是利用沿海灾害系统 (CHS) 的北大西洋海岸综合研究 (NACCS) 数据库进行的。结果表明，实现的数值效率是显着的，将计算负担减少了 50-250 倍，而对估计的危险曲线的准确性只有中等影响。验证研究是利用沿海灾害系统 (CHS) 的北大西洋海岸综合研究 (NACCS) 数据库进行的。结果表明，实现的数值效率是显着的，将计算负担减少了 50-250 倍，而对估计的危险曲线的准确性只有中等影响。