Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework

Computational hydrodynamic models support river science and management. However, current physics-based models face computational challenges; they require extensive processing time for large-scale two-dimensional flood simulations. Despite the success of Deep Learning (DL) applications in generating inundation maps, accurate prediction of unsteady flood hydrodynamic maps remains challenging. This paper compares traditional approaches to a novel DL approach, which integrates convolutional neural networks with long short-term memory, to deliver precise, rapid, and continuous simulation of the spatiotemporal dynamics of river floods. This is the first DL framework able to generate essential hydrodynamic variables: water depth, velocity magnitude, and flow direction maps. Water depth and velocity magnitude predictions across the testing dataset are robust, with average RMSE of 0.14 m and 0.02 m/s, respectively. The DL predictions are 415 times faster compared to traditional computational approaches, representing a paradigm shift in hydrodynamics modeling that advances long-term flood simulations and resilient river management. • This paper compares a novel hydrodynamic DL framework to a physics-based model. • CNNs are integrated with LSTMs to capture spatiotemporal dynamics of flood events. • The framework enables rapid and reliable generation of hydrodynamic parameters. • The approach generates water depth, velocity magnitude, and flow direction maps.