Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3823-2023
https://doi.org/10.5194/hess-27-3823-2023
Research article
 | 
27 Oct 2023
Research article |  | 27 Oct 2023

Forecasting estuarine salt intrusion in the Rhine–Meuse delta using an LSTM model

Bas J. M. Wullems, Claudia C. Brauer, Fedor Baart, and Albrecht H. Weerts

Data sets

Data underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model B. Wullems, C. Brauer, F. Baart, and A. Weerts https://doi.org/10.4121/21944249

KNMI (Royal Netherlands Meteorological Institute) - Daggegevens van het weer in Nederland KNMI https://www.knmi.nl/nederland-nu/klimatologie/daggegevens

Waterbeheer (expert) - Rijkswaterstaat waterinfo Rijkswaterstaat https://waterinfo.rws.nl/#!/kaart/Waterbeheer/

Model code and software

Data underlying the publication: Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model B. Wullems, C. Brauer, F. Baart, and A. Weerts https://doi.org/10.4121/21946724

Machine learning model for predicting salt concentrations in the Rhine-Meuse delta B. Wullems, C. Brauer, F. Baart, and A. Weerts https://doi.org/10.5281/zenodo.10017846

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Short summary
In deltas, saltwater sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine–Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.