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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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| Created: | Feb 06, 2026 at 4:42 a.m. (UTC) | |
| Last updated: | Feb 06, 2026 at 6:59 p.m. (UTC) | |
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| Content types: | CSV Content |
| Sharing Status: | Public |
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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.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.txt
README The instructions for reproducing each experiment are provided below. * 1st Experiment - Download the file '40_flood-prone_streets.zip'. - Download the file '1st_experiment.ipynb'. This notebook is currently configured for the Global Model. - Refer to 'Clustering_result.docx' and cluster the 40 streets according to the provided clustering results. - Modify '1st_experiment.ipynb' as needed based on the number of streets being predicted. * 2nd Experiment - The second experiment focuses on Cluster 4 (K = 8), which includes 6 streets. - Download the file '2nd_experiment.ipynb'. This notebook is currently configured such that the water depth feature at time t−1 is excluded for all six streets. - Run the code while varying which water depth features at time t−1 are included. * 3rd Experiment - Download 'FID_6508.csv' and '3rd_experiment.ipynb'. - The current version of '3rd_experiment.ipynb' is configured to include Street 6508 in the global model. - Modify the code to match the number of streets in the cluster to which street 6508 is assigned, and then run the experiment.
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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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