the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Forecasting estuarine salt intrusion in the Rhine-Meuse delta using an LSTM model
Bas Johan Marinus Wullems
Claudia Catharina Brauer
Fedor Baart
Albrecht Henricus Weerts
Abstract. Estuarine salt intrusion causes problems with freshwater availability in many deltas. Water managers require timely and accurate forecasts to be able to mitigate and adapt to salt intrusion. Data-driven models derived with machine learning are ideally suited for this, as they can mimic complex non-linear systems and are computationally efficient. We set up a Long Short Term Memory (LSTM) model to forecast salt intrusion in the Rhine-Meuse delta. It forecasts chloride concentrations up to 7 days ahead at Krimpen aan den IJssel, an important location for freshwater provision. The model forecasts baseline concentrations and peak timing well, but peak height is underestimated, a problem that becomes worse with increasing lead time. Between lead times of 1 and 7 days, forecast precision declines from 0.9 to 0.7 and forecast recall declines from 0.7 to 0.5 on average. Given these results, we aim to extend the model to other locations in the delta. We expect that a similar setup can work in other deltas, especially those with a similar or simpler channel network.
Bas Johan Marinus Wullems et al.
Status: open (until 26 Apr 2023)
Bas Johan Marinus Wullems et al.
Data sets
Salt LSTM Data Bas Wullems https://figshare.com/s/0d06645e003d9e8f9e7e
Model code and software
Salt intrusion LSTM Bas Wullems https://figshare.com/s/a4701fa181b015eb6c69
Bas Johan Marinus Wullems et al.
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