  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.
