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

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

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Categories

• Civil Engineering
• Engineering
• Information and Computing Sciences

Keywords

Dike breach Flood inundation LSTM Machine learning

Geolocation

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Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

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by L.S. Besseling , A. Bomers, S. J. M. H. Hulscher

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

Associated peer-reviewed publication

Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network

References

• https://doi.org/10.3390/hydrology8020087

Organizations

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