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Input data for Physics-Informed LSTM surrogate model for street-scale flood forecasting in Norfolk, Virginia
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| Type: | Resource | |
| Storage: | The size of this resource is 4.1 MB | |
| Created: | Mar 09, 2026 at 4:35 p.m. (UTC) | |
| Last updated: | Sep 09, 2026 at 7:22 a.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Public |
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| Views: | 943 |
| Downloads: | 95 |
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Abstract
This dataset contains input data (20 storm events) for developing a Physics-Informed LSTM (PI LSTM) surrogate model to forecast street-scale nuisance flooding in Norfolk, Virginia, USA. There are five files in this resource -
1. The "street_shapefiles.zip" folder includes a shapefile of the street segments (polygons of 50 m length x 7.2 m width) of Norfolk. Alongside, it includes a "D0_R40_S22.csv" file containing 22 flood-prone streets selected from the STORM report.
2. The "vol_wd_22_rh_q.zip" folder includes the input CSV files for the top 20 daily storm events from 2016-2018 for the streets of Norfolk.
3. The "relational_database.zip" folder includes three CSV files for node_data (varied spatially), tide_data (varied temporally), and weather_data (varied spatially and temporally) for efficient data management.
4. The Python script "create_relational_data.py" is used to convert "vol_wd_22_rh_q" CSvs to "relational_database" CSVs.
5. The "variable_description.csv" file describes the input variables used in the "vol_wd_22_rh_q.zip" and "relational_database.zip" CSV files, along with their units.
The Python script of the PI LSTM model is available on GitHub https://github.com/br3xk/Physics-Informed-LSTM-surrogate-model-for-real-time-street-scale-flood-forecasting
Subject Keywords
Coverage
Spatial
Temporal
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How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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