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

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)