Publications
Publication Information
| Title | A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia |
| Authors | Helen McSpadden, Steven Goldenberg, Malachi Schram, Binata Roy, Jonathan Goodall, Heather Richter |
| JLAB number | JLAB-CST-23-3914 |
| LANL number | arXiv:2307.14185 |
| Other number | DOE/OR/23177-7116 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Other |
| Other Funding: | ODU |
|
Journal Compiled for Machine Learning with Applications Volume 15 Page(s) 100518 | |
| Publication 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. |
| Experiment Numbers: | other |
| Group: | Data Science |
| Document: | |
| DOI: | https://doi.org/10.1016/j.mlwa.2023.100518 |
| Accepted Manuscript: | 1-s2.0-S2666827023000713-main.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |