Code underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model

doi: 10.4121/21946724.v3
The doi 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: 10.4121/21946724
Datacite citation style:
Wullems, Bas; Brauer, Claudia; Albrecht Weerts; Baart, Fedor (2023): Code underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model. Version 3. 4TU.ResearchData. software. https://doi.org/10.4121/21946724.v3
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
choose version:
version 3 - 2023-12-05 (latest)
version 2 - 2023-09-25 version 1 - 2023-09-08
Wageningen University and Research logo
usage stats
216
views
50
downloads
geolocation
Netherlands
lat (N): 51.5 ... 52.5
lon (E): 3.5 ... 6.0
time coverage
2011-2020
licence
cc-0.png logo CC0

 

Machine learning model for predicting salt concentrations in the Rhine-Meuse delta.

The folder 'Data' contains processed data, identical to 'Features.csv' in the raw dataset.

The folder 'Models' contains an ensemble of LSTM models created with the script 'LSTMv1.py'.

The script 'preprocessing.py' was used to convert the raw data to the daily data in 'Features.csv'.

history
  • 2023-09-08 first online
  • 2023-12-05 published, posted
publisher
4TU.ResearchData
format
Python scripts, models created in python, csv and txt data files
funding
  • NWO perspectief P18-32
organizations
Wageningen University and Research, Hydrology and Quantitative Water Management Group
Deltares, Department of Operational Water Management & Early Warning, Unit of Inland Water Systems
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Hydraulic Engineering

DATA

files (1)