Flood inundation forecast provides critical information for emergency
planning before and during flood events. Real time flood inundation forecast
tools are still lacking. High-resolution hydrodynamic modeling has become more
accessible in recent years, however, predicting flood extents at the street and
building levels in real-time is still computationally demanding. Here we
present a hybrid process-based and data-driven machine learning (ML) approach
for flood extent and inundation depth prediction. We used the Fourier neural
operator (FNO), a highly efficient ML method, for surrogate modeling. The FNO
model is demonstrated over an urban area in Houston (Texas, U.S.) by training
using simulated water depths (in 15-min intervals) from six historical storm
events and then tested over two holdout events. Results show FNO outperforms
the baseline U-Net model. It maintains high predictability at all lead times
tested (up to 3 hrs) and performs well when applying to new sites, suggesting
strong generalization skill.

我们提出了一种基于混合过程和数据驱动的机器学习方法，用于洪水范围和淹没深度的预测，该方法使用傅里叶神经运算器进行代理建模，并在休斯敦的城区进行了演示测试。结果表明，傅里叶神经运算器模型优于基准的 U-Net 模型，并具有较强的泛化能力。