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Hydrological Modelling with Machine Learning

  • 1 January, 2022
  • Wageningen University, Environmental Systems Analysis
  • prof. dr LG Hein
  • Dr C Duku

Ecosystem degradation and restoration affect hydrological ecosystem services world-wide, but there is still a scarcity of scalable and accurate modelling approaches to assess how land use change affects flood risk mitigation and other ecosystem services. This research aims at developing a spatiotemporal run-off model that is broadly applicable, accurate, and flexible in terms of data requirements. This model will be able to analyse hydrological ecosystem services provided by the vegetation in upper watersheds, and show how different land use change scenarios will influence flood risks.. The model will be developed for major watersheds in Europe and Brazil.


Jelle ten Harkel

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