Document Type

Article

Publication Date

2024

DOI

10.1016/j.mlwa.2023.100518

Publication Title

Machine Learning with Applications

Volume

15

Pages

100518 (1-10)

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 personal and 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). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

Rights

© 2023 The Authors.

This is an open access article under the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Data Availability

Article states: "The authors do not have permission to share data."

Original Publication Citation

McSpadden, D., Goldenberg, S., Roy, B., Schram, M., Goodall, J. L., & Richter, H. (2024). A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia. Machine Learning with Applications, 15, 1-10, Article 100518. https://doi.org/10.1016/j.mlwa.2023.100518

ORCID

0000-0003-1812-0572 (Richter)

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