2017
DOI: 10.1201/9781315115979
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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

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Cited by 10 publications
(3 citation statements)
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References 221 publications
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“…However, none of the previous studies assessed the usefulness of real-time CS observations in improving flood predictions. First attempts are reported in Mazzoleni et al (2015a,b; and Mazzoleni (2017), where the authors Hydrol. Earth Syst.…”
Section: Overview In Data Assimilation and Crowdsourced Observationsmentioning
confidence: 99%
“…However, none of the previous studies assessed the usefulness of real-time CS observations in improving flood predictions. First attempts are reported in Mazzoleni et al (2015a,b; and Mazzoleni (2017), where the authors Hydrol. Earth Syst.…”
Section: Overview In Data Assimilation and Crowdsourced Observationsmentioning
confidence: 99%
“…However, due to their relatively low reliability, crowdsourced observations have not been widely integrated into flood forecasting models. Instead, they have been used to validate model results against observations (Mazzoleni 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Characterization of the tide could be done by a hydrodynamic modelling or data-driven approach (DDA) [10]. The hydrodynamic models produce a numerical solution of one or more of the governing differential equations of continuity, momentum, and energy of fluid [11,12], while the DDA helps us to find coherent patterns of temporal behavior that could be used to characterize physical processes [10].…”
Section: Introductionmentioning
confidence: 99%