The objective of this study is find out whether maximum daily discharge of the Geul and Rur catchments can be forecast using machine learning (ML) methods, and if so, to what extent. In addition, these ML models are compared to a conceptual model to see which performs better. A second objective is to test whether soil moisture content (SMC) and NDVI increase performance of the twoMLmodels. The Geul and Rur catchments are both partly situated in the administrative area of Waterschap Limburg, a water authority in the Netherlands. They use discharge forecasts in order to prepare flood defenses and to monitor high water levels more closely. Currently, discharge is forecast using the conceptual HBV model for the Geul. Forecasting is done based on experience for the Rur and only in case of high water levels. Conceptual and physical models are based on physical laws, i.e. conservation of mass and energy. However, some relations are not yet fully understood, or are hard to translate to equations, and assumptions have to be made. This is why a data-driven approach is used, as no explicit relationship between variables has to be specified.
Er zijn nog geen reacties. U kunt de eerste schrijven!
Schrijf een reactie
To support researchers to publish their research Open Access, deals have been negotiated with various publishers. Depending on the deal, a discount is provided for the author on the Article Processing Charges that need to be paid by the author to publish an article Open Access. A discount of 100% means that (after approval) the author does not have to pay Article Processing Charges.
For the approval of an Open Access deal for an article, the corresponding author of this article must be affiliated with Wageningen University & Research.
U moet eerst inloggen om gebruik te maken van deze service. Login als Wageningen University & Research user of guest user rechtsboven op deze pagina.