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Input data for LSTM and seq2seq LSTM surrogate models for multi-step-ahead street-scale flood forecasting in Norfolk, VA
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| Type: | Resource | |
| Storage: | The size of this resource is 345.5 MB | |
| Created: | Aug 30, 2023 at 6:12 p.m. | |
| Last updated: | Sep 27, 2023 at 4:10 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 265 |
| Downloads: | 35 |
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Abstract
This resource includes the input data for LSTM and seq2seq LSTM surrogate models for multi-step-ahead street-scale flood forecasting in Norfolk, VA, USA. The data consists of topographic features: topographic wetness index (TWI), depth to water (DTW) and elevation, and environmental features: hourly rainfall and tide level from gauge stations and water depth generated by the physics-based model TUFLOW.
There are three folders in this resource -
1. The "OriginalData" folder includes the CSV files for the top 20 storm events from 2016-2018 for the streets of Norfolk.
2. The "FloodproneStreets" folder includes shapefiles of the street segments (polygons of 7.2 m width x 50 m length) of Norfolk. Alongside, it includes a CSV file containing 22 flood-prone streets selected from the STORM report.
3. The "RelationalDatabase" folder includes three CSV files for node_data (varied spatially), tide_data (varied temporally) and weather_data tide_data (varied spatially and temporally) for efficient data management. The notebook script "create_relational_data.ipynb" is used to convert "OriginalData" to "RelationalDatabase".
The Python script of the LSTM and seq2seq LSTM surrogate models is available on GitHub https://github.com/br3xk/LSTM-and-seq2seq-LSTM-surrogate-models-for-street-scale-flood-forecasting
The output of the model is forecasted hourly water depth on the 22 flood-prone streets with 4-hr and 8-hr lead.
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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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