Improving streamflow prediction for hydrological drought forecasting through a machine learning approach : A case study of Rhine River, the Netherlands
Drought forecasting is crucial for effectively managing water resources. However, streamflow forecasting remains challenging in regions like the Rhine Delta in the Netherlands, where forecasting accuracy and lead time are still limited. To address these issues, we present a machine learning (ML) approach based on the Long Short-Term Memory (LSTM) algorithm. We examine various combinations of univariate and multivariate configurations to improve streamflow forecasts for the Rhine River, focusing on sub-seasonal to seasonal lead times. In various experiments, we explore stacked LSTM setups with... Mehr ...
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Dokumenttyp: | Thesis Master of Science
IHE Delft Institute for Water Education Delft the Netherlands; |
Erscheinungsdatum: | 2023 |
Verlag/Hrsg.: |
Delft : IHE Delft Institute for Water Education;
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Schlagwörter: | drought / drought forecasting / machine learning / the Netherlands / long short-term memory / streamflow prediction |
Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-27587375 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.25831/5vn8-4163 |