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

Maurizio Mazzoleni

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

Maurizio Mazzoleni

Water Resources

Research output: Thesis › Dissertation (TU Delft)

264 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 language	English
Awarding Institution	Delft University of Technology
Supervisors/Advisors	Solomatine, D.P., Promotor Alfonso, L, Copromotor, External person
Award date	28 Nov 2016
Publisher	CRC Press / Balkema - Taylor & Francis Group
Print ISBNs	978-1-138-03590-4
Publication status	Published - 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.

Access to Document

2016_UNESCO-IHE_PHD_THESIS_MAZZOLENI_iFinal published version, 31 MBLicence: CC BY-NC

Cite this

@phdthesis{e4ae403572dc41ccbad0eab044e0613a,

title = "Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models",

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.",

author = "Maurizio Mazzoleni",

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.",

year = "2016",

language = "English",

isbn = "978-1-138-03590-4",

publisher = "CRC Press / Balkema - Taylor \& Francis Group",

type = "Dissertation (TU Delft)",

school = "Delft University of Technology",

}

TY - THES

T1 - Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models

AU - Mazzoleni, Maurizio

N1 - 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.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://resolver.tudelft.nl/uuid:e4ae4035-72dc-41cc-bad0-eab044e0613a

M3 - Dissertation (TU Delft)

SN - 978-1-138-03590-4

PB - CRC Press / Balkema - Taylor & Francis Group

ER -

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

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this