A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia
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Abstract (arXiv)
Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation underscores the importance of using a model architecture that supports the communication of prediction uncertainty and the effective integration of relevant, multi-modal features.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 11 of its 58 references that are in the map. First posted on arXiv 26 Jul 2023.
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11 of 58 references.
References · 11
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