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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models (Paperback)
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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models (Paperback)
Series: IHE Delft PhD Thesis Series
Expected to ship within 12 - 17 working days
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In recent years, the continued technological advances have led to
the spread of low-cost sensors and devices supporting crowdsourcing
as a way to obtain observations of hydrological variables in a more
distributed way than the classic static physical sensors. The main
advantage of using these type of sensors is that they can be used
not only by technicians but also by regular citizens. However, due
to their relatively low reliability and varying accuracy in time
and space, crowdsourced observations have not been widely
integrated in hydrological and/or hydraulic models for flood
forecasting applications. Instead, they have generally been used to
validate model results against observations, in post-event
analyses. This research aims to investigate the benefits of
assimilating the crowdsourced observations, coming from a
distributed network of heterogeneous physical and social (static
and dynamic) sensors, within hydrological and hydraulic models, in
order to improve flood forecasting. The results of this study
demonstrate that crowdsourced observations can significantly
improve flood prediction if properly integrated in hydrological and
hydraulic models. This study provides technological support to
citizen observatories of water, in which citizens not only can play
an active role in information capturing, evaluation and
communication, leading to improved model forecasts and better flood
management.
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