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

• URL: http://arxiv.org/abs/2307.14185v1
• Date: Wed, 26 Jul 2023 13:24:01 GMT
• Title: A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia
• Authors: Diana McSpadden and Steven Goldenberg and Binata Roy and Malachi Schram and Jonathan L. Goodall and Heather Richter
• Abstract summary: Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides. High-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, but their computational complexity renders them unsuitable for real-time applications. This study compares the performance of a surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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