Data accompanying the publication: Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network

DOI:10.4121/6fd289d8-ec0e-4dd9-94fd-4566783e9c3d.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/6fd289d8-ec0e-4dd9-94fd-4566783e9c3d
Datacite citation style:
Besseling, L.S.; Bomers, A.; Hulscher, S. J. M. H. (2024): Data accompanying the publication: Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6fd289d8-ec0e-4dd9-94fd-4566783e9c3d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

University of Twente logo

Usage statistics

109
views
143
downloads

Geolocation

IJssel river near Westervoort, the Netherlands
lat (N): 51.961558163389796
lon (E): 5.957857705105184
view on openstreetmap

Licence

CC BY 4.0

This dataset contains all necessary data to produce the output presented in the paper "Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network", by L.S. Besseling, A. Bomers and S.J.M.H. Hulscher, published in Hydrology (2024). Included are the code for creating the LSTM neural network, the dataset from a 1D2D hydrodynamic HEC-RAS model on which the network was trained and tested, and helper files for running the code and visualizing results. A more detailed description of the dataset is provided in the Readme. For any further questions on the data, please contact the authors.

History

  • 2024-09-16 first online, published, posted

Publisher

4TU.ResearchData

Format

readme/.txt scripts/.py anaconda-environment/.yaml LSTMmodel/.zip simulationdata/.zip shapefile/.zip

Organizations

University of Twente, Faculty of Engineering Technology (ET), Department of Water Engineering & Management

DATA

Files (7)