Accurate flood inundation modeling using a complex high-resolution hydrodynamic (high-fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modeling the dynamical behavior of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial-temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low-resolution hydrodynamic (low-fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low-fidelity model is computationally efficient but has reduced accuracy compared to a high-fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low-fidelity modeling results. To address the challenges posed by the high dimensionality of the data from the low- and high-fidelity models, Empirical Orthogonal Functions analysis is applied to reduce the spatial-temporal data into a few key features. This enables training of the Sparse GP model to predict high-fidelity flood data from low-fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high-fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low-fidelity model and incurred only 39% of the computational cost of a high-fidelity model.

中文翻译：

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通过空间分析和高斯过程学习提升洪水淹没的低保真水动力模型

使用复杂的高分辨率水动力（高保真）模型进行准确的洪水淹没建模可能对计算要求很高。为了解决这个问题，已经开发了有效的近似方法（代理模型）。尽管最近取得了进展，但在使用替代方法以有效方式模拟洪水淹没的动态行为方面仍然存在重大挑战。由于数据的高时空维度，大多数方法都侧重于估计最大洪水范围。本研究提出了一种混合替代模型，由低分辨率流体动力学（低保真）和稀疏高斯过程（Sparse GP）模型组成，以捕捉洪水范围的动态演变。与高保真模型相比，低保真模型的计算效率很高，但准确性降低。为了解决精度降低的问题，使用稀疏 GP 模型来纠正低保真建模结果。为了解决来自低保真和高保真模型的数据的高维带来的挑战，应用经验正交函数分析将时空数据简化为几个关键特征。这使得 Sparse GP 模型的训练能够从低保真洪水数据中预测高保真洪水数据，从而使混合替代模型可以在不使用高保真模型的情况下准确模拟动态洪水范围。混合替代模型在澳大利亚平坦而复杂的 Chowilla 洪泛区得到验证。与仅使用低保真模型相比，混合模型显着改善了结果，并且仅产生了高保真模型的 39% 的计算成本。