STI Publications - View Publication Form #19194

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Publication Information
Title A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia
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.
Author(s) Diana McSpadden, Steven Goldenberg, Malachi Schram, Binata Roy, Jonathan Goodall, Heather Richter
Publication Date March 2024
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Comp Sci&Tech (CST) Div / Data Science / Data Science
Funding Source Other, ODU
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-CST-23-3914 OSTI Number: 2339786
LANL Number: arXiv:2307.14185 Other Number: DOE/OR/23177-7116
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Machine Learning with Applications
Refereed No
Volume 15
Issue
Page(s) 100518
Attachments/Datasets/DOI Link
Document(s)
SLF_arXiv_072420231.pdf (STI Document)
1-s2.0-S2666827023000713-main.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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