TY - DATA
T1 - Data accompanying the publication: Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network
PY - 2024/09/16
AU - L.S. Besseling
AU - A. Bomers
AU - S. J. M. H. Hulscher
UR - 
DO - 10.4121/6fd289d8-ec0e-4dd9-94fd-4566783e9c3d.v1
KW - Dike breach
KW - Flood inundation
KW - Machine learning
KW - LSTM
N2 - <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>
ER -