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Low-lying coastal cities, exemplified by Norfolk, Virginia, face thechallenge of street flooding caused by rainfall and tides, which straintransportation and sewer systems and can lead to property damage. Whilehigh-fidelity, physics-based simulations provide accurate predictions of urbanpluvial flooding, their computational complexity renders them unsuitable forreal-time applications. Using data from Norfolk rainfall events between 2016and 2018, this study compares the performance of a previous surrogate modelbased on a random forest algorithm with two deep learning models: LongShort-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigationunderscores the importance of using a model architecture that supports thecommunication of prediction uncertainty and the effective integration ofrelevant, multi-modal features.

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia