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: 10.4121/21946724

(2023): Code underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/21946724.v1

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Keywords

Geolocation

Time coverage

2011-2020

Licence

CC0

Interoperability

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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, published, posted

Publisher

4TU.ResearchData

Format

Python scripts, models created in python, csv and txt data files

Associated peer-reviewed publication

Predicting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model

Data link

https://github.com/BasWullems/salt_intrusion_lstm

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