Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of
storm events. However, the computationally intensive numerical solutions
required for high-resolution hydrodynamics have historically prevented their
implementation in near-real-time flood forecasting. This study examines whether
several Deep Neural Network (DNN) architectures are suitable for optimizing
hydrodynamic flood models. Several pluvial flooding events were simulated in a
low-relief high-resolution urban environment using a 2D HEC-RAS hydrodynamic
model. These simulations were assembled into a training set for the DNNs, which
were then used to forecast flooding depths and velocities. The DNNs' forecasts
were compared to the hydrodynamic flood models, and showed good agreement, with
a median RMSE of around 2 mm for cell flooding depths in the study area. The
DNNs also improved forecast computation time significantly, with the DNNs
providing forecasts between 34.2 and 72.4 times faster than conventional
hydrodynamic models. The study area showed little change between HEC-RAS' Full
Momentum Equations and Diffusion Equations, however, important numerical
stability considerations were discovered that impact equation selection and DNN
architecture configuration. Overall, the results from this study show that DNNs
can greatly optimize hydrodynamic flood modeling, and enable near-real-time
hydrodynamic flood forecasting.

本文研究使用 DNN 优化水动力洪水模型，通过在 2D HEC-RAS 水动力模型中模拟洪水事件并使用 DNN 预测洪水深度和速度，结果显示 DNN 能够极大地优化水动力洪水模型，实现接近实时的洪水预报，且可以显著提高预报计算时间。同时，该研究还发现了影响方程选择和 DNN 架构配置的数值稳定性问题。