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Input data and code script for comparing strategies for training LSTM models for street-scale flood prediction in Norfolk, Virginia
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| Type: | Resource | |
| Storage: | The size of this resource is 590.6 KB | |
| Created: | Feb 06, 2026 at 4:42 a.m. (UTC) | |
| Last updated: | Apr 17, 2026 at 5:16 p.m. (UTC) (Metadata update) | |
| Published date: | Apr 17, 2026 at 5:16 p.m. (UTC) | |
| DOI: | 10.4211/hs.f384de0d4bb248ba915eac7103822f9d | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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| Views: | 466 |
| Downloads: | 84 |
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Abstract
These are tabular input data and python code script for LSTM surrogate model built for real-time street flood prediction in Norfolk, VA. The LSTM surrogate model approximates water depth on streets generated by TUFLOW. The inputs of the model are topographic feature: elevation, and environmental features such as hourly rainfall, cumulative rainfall in previous hours, hourly tide level, etc. The output of the model is hourly water depth on streets during storm events generated by the TUFLOW model.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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