Record nummer 2289418
Titel Forecasting river discharge using machine learning methods : with application to the Geul and Rur river
Auteur(s) Munckhof, G. van den
Uitgever Delft : Technische Universiteit Delft
Jaar van uitgave 2020
Pagina's 125 p
Online full text
Publicatie type Studentenverslag
Taal Engels
Toelichting (Engels) 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.
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