Abstract：
Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.
摘要：
水动力模型可以准确地模拟洪水淹没，但受限于其高计算需求，该需求与模型复杂性呈非线性关系。决议,和域大小。因此,使用高分辨率水动力模型进行实时洪水预测或在概率洪水设计需要大量预测时通常是不可行的。已经开发了计算有效的代理模型来解决这个问题。最近开发的低保真度，空间分析,高斯过程学习（LSG）模型在计算效率和仿真精度方面都表现出了很强的性能。LSG模型是物理指导的代理模型，通过首先使用极其粗略和简化（即低保真度）的水动力模型来模拟洪水淹没，以提供洪水淹没的初始估计。然后，通过经验正交函数（EOF）分析和稀疏高斯过程模型，低保真度估计可以提供准确的高分辨率预测。尽管迄今为止取得了可喜的成果，LSG模型尚未与其他代理模型进行基准测试。需要进行这种比较才能充分了解LSG模型的价值，并为未来洪水淹没模拟的研究工作提供指导。本研究将LSG模型与四种最先进的替代洪水淹没模型进行了比较。评估代理模型在模型训练范围之内和之外的事件中模拟洪水淹没的时间和空间演变的能力。这些模型在澳大利亚和英国进行了三个不同的案例研究。发现LSG模型在洪水范围和水深方面都具有较高的准确性，包括当应用于所使用的训练数据范围之外的洪水事件时，同时实现高计算效率。此外,发现低保真度模型在实现LSG模型的整体卓越性能中起着至关重要的作用。
