%0 Generic
%A Besseling, L.S.
%A Bomers, A.
%A Hulscher, S. J. M. H.
%D 2024
%T Data accompanying the publication: Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network
%U 
%R 10.4121/6fd289d8-ec0e-4dd9-94fd-4566783e9c3d.v1
%K Dike breach
%K Flood inundation
%K Machine learning
%K LSTM
%X <p>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.</p>
%I 4TU.ResearchData