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.

翻译：水文水动力学洪水模拟可提升对风暴事件的水文与水力预测能力。然而，高分辨率水动力学所需的计算密集型数值求解方法历来制约其在近实时洪水预报中的应用。本研究探讨了多种深度神经网络架构是否适用于优化水动力学洪水模型。通过二维HEC-RAS水动力学模型，在低起伏高分辨率城市环境中模拟了多次暴雨洪水事件。将这些模拟结果构建为深度神经网络的训练集，并用于预测洪水深度与流速。将深度神经网络的预测结果与水动力学洪水模型进行对比，二者吻合良好，研究区内单元格洪水深度的中位数均方根误差约为2毫米。深度神经网络显著提升了预测计算效率，其预测速度比传统水动力学模型快34.2至72.4倍。研究区内HEC-RAS全动量方程与扩散方程的计算结果差异较小，但发现方程选择与深度神经网络架构配置受数值稳定性因素影响显著。总体而言，本研究结果表明深度神经网络可大幅优化水动力学洪水建模，并实现近实时水动力学洪水预报。