Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.

替代模型在风暴潮预测中变得越来越流行。这些模型使用现有的风暴模拟数据库，通常在区域洪水研究期间开发，提供快速计算、数据驱动的近似值，量化任何新风暴（未包含在训练数据库中）的预期风暴潮。本文考虑为特拉华湾开发这种替代模型，使用由合成热带气旋驱动的 156 次模拟的数据库，并为包含感兴趣地理区域内近 300,000 个计算节点的网格提供预测。采用克里金法（高斯过程回归）作为代理建模技术，并建立了各种相关的进步。检查合成风暴数据库的适当参数化。为了这，代替登陆时的风暴特征，风暴与感兴趣域的某个代表性点距离最近时的特征被研究作为替代参数化，并被发现产生更好的替代。对于在某些数据库风暴中保持干燥的节点，使用加权k最近邻 ( k NN) 插值被认为是填充缺失的数据。结合逻辑主成分分析和克里金法的二级分类代理模型的使用进行了检查，以解决推算激增导致节点条件错误分类的情况。最后，讨论了与替代模型过度拟合相关的问题，这源于可用数据库的小规模。这些问题扩展到代理模型超参数的校准以及所采用的验证方法。在此过程中，详细检查了使用主成分分析作为降维技术的好处，以及浪涌输出的适当转换和缩放。