Input Data for LSTM Model for Street-Scale Nuisance Flood Forecasting in Norfolk, Virginia using Transfer Learning

This dataset contains the input data (50 storm events) for an LSTM model used for street-scale nuisance flood forecasting in Norfolk, Virginia, USA, using transfer learning. The model inputs include topographic features such as the Topographic Wetness Index (TWI), Depth To Water (DTW), and elevation, as well as environmental features like hourly rainfall and hourly tide levels. Additionally, the input includes hourly street-level water depth during storm events, generated by the 1D/2D coupled hydrodynamic model TUFLOW.

1. The \"Streetshapefile\" folder includes shapefile of the street segments (polygons of 50 m length x 7.2 m width) of Norfolk. Alongside, it includes source and target flood-prone streets CSV files, selected from the STORM flood report.

2. The \"OriginalData_TL\" folder includes the CSV files for the top 50 daily storm events from 2016 - 2023 for the streets of Norfolk.

3. The \"RelationalDatabase\" 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 notebook script \"create_relational_data_bin.py\" is used to convert \"OriginalData\" to \"RelationalDatabase\".

5. The \"Experiments_TL\" folder contains the % of events and % of streets used for different experiments/ training configurations.

The python script of the LSTM transfer models is available on GitHub https://github.com/br3xk/Applying-Transfer-Learning-for-Street-Scale-Nuisance-Flood-Forecasting-in-Coastal-Urban-Cities/edit/main/README.md