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• W2784225651 abstract "Abstract. To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial–temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction–validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the usefulness of integrating crowdsourced observations. First, the assimilation of crowdsourced observations located at upstream points of the Bacchiglione catchment ensure high model performance for high lead-time values, whereas observations at the outlet of the catchments provide good results for short lead times. Second, biased and inaccurate crowdsourced observations can significantly affect model results. Third, the theoretical scenario of citizens motivated by their feeling of belonging to a community of friends has the best effect in the model performance. However, flood prediction only improved when such small communities are located in the upstream portion of the Bacchiglione catchment. Finally, decreasing involvement over time leads to a reduction in model performance and consequently inaccurate flood forecasts." @default.
• W2784225651 created "2018-01-26" @default.
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• W2784225651 date "2018-01-17" @default.
• W2784225651 modified "2023-10-18" @default.
• W2784225651 title "Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)" @default.
• W2784225651 cites W1507471273 @default.
• W2784225651 cites W1602658565 @default.
• W2784225651 cites W1966262430 @default.
• W2784225651 cites W1968545881 @default.
• W2784225651 cites W1969702679 @default.
• W2784225651 cites W1971491685 @default.
• W2784225651 cites W1972327193 @default.
• W2784225651 cites W1987172718 @default.
• W2784225651 cites W1988756414 @default.
• W2784225651 cites W1989124325 @default.
• W2784225651 cites W1990612304 @default.
• W2784225651 cites W2002710768 @default.
• W2784225651 cites W2003696872 @default.
• W2784225651 cites W2008458265 @default.
• W2784225651 cites W2020196977 @default.
• W2784225651 cites W2028464044 @default.
• W2784225651 cites W2033904036 @default.
• W2784225651 cites W2041454412 @default.
• W2784225651 cites W2050424921 @default.
• W2784225651 cites W2058998445 @default.
• W2784225651 cites W2066937586 @default.
• W2784225651 cites W2070704253 @default.
• W2784225651 cites W2075712036 @default.
• W2784225651 cites W2077172418 @default.
• W2784225651 cites W2087444761 @default.
• W2784225651 cites W2090818917 @default.
• W2784225651 cites W2090948918 @default.
• W2784225651 cites W2094516009 @default.
• W2784225651 cites W2095581768 @default.
• W2784225651 cites W2098098480 @default.
• W2784225651 cites W2098455013 @default.
• W2784225651 cites W2105663391 @default.
• W2784225651 cites W2105934661 @default.
• W2784225651 cites W2109890866 @default.
• W2784225651 cites W2114971337 @default.
• W2784225651 cites W2115545952 @default.
• W2784225651 cites W2127901650 @default.
• W2784225651 cites W2127970560 @default.
• W2784225651 cites W2130015732 @default.
• W2784225651 cites W2139389869 @default.
• W2784225651 cites W2140730595 @default.
• W2784225651 cites W2141434715 @default.
• W2784225651 cites W2160093511 @default.
• W2784225651 cites W2161722241 @default.
• W2784225651 cites W2165445075 @default.
• W2784225651 cites W2168993830 @default.
• W2784225651 cites W2176419970 @default.
• W2784225651 cites W2232442249 @default.
• W2784225651 cites W2238169501 @default.
• W2784225651 cites W2270112113 @default.
• W2784225651 cites W2315561481 @default.
• W2784225651 cites W235376 @default.
• W2784225651 cites W2360033652 @default.
• W2784225651 cites W2413742060 @default.
• W2784225651 cites W2499133386 @default.
• W2784225651 cites W2509484033 @default.
• W2784225651 cites W2514092390 @default.
• W2784225651 cites W2528154185 @default.
• W2784225651 cites W2531442614 @default.
• W2784225651 cites W2549412929 @default.
• W2784225651 cites W2557668118 @default.
• W2784225651 cites W2558186269 @default.
• W2784225651 cites W2580774552 @default.
• W2784225651 cites W2588083849 @default.
• W2784225651 cites W2595936615 @default.
• W2784225651 cites W2625996986 @default.
• W2784225651 cites W2731171009 @default.
• W2784225651 cites W3021439089 @default.
• W2784225651 cites W4230710065 @default.
• W2784225651 cites W4240905454 @default.
• W2784225651 cites W4249617805 @default.
• W2784225651 cites W787719797 @default.
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• W2784225651 doi "https://doi.org/10.5194/hess-22-391-2018" @default.
• W2784225651 hasPublicationYear "2018" @default.
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