Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models

Maurizio Mazzoleni

Research output: ThesisDissertation (TU Delft)

88 Downloads (Pure)

Abstract

Monitoring stations have been used for decades to measure hydrological variables,
and mathematical water models used to predict floods can be enhanced by the
incorporation of these observations, i.e. by data assimilation. The assimilation of
remotely sensed water level observations in hydrological and hydraulic modelling
has become more attractive due to their availability and spatially distributed nature.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Solomatine, D.P., Supervisor
  • Alfonso, L, Advisor, External person
Award date28 Nov 2016
Publisher
Print ISBNs978-1-138-03590-4
Publication statusPublished - 2016

Bibliographical note

Dissertation submitted in fulfilment of the requirements of the Board for Doctorates of Delft University of Technology and of the Academic Board of the UNESCO-IHE Institute for Water Education.

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