Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models (ePub)
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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 can be a potential application of recent efforts to build citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, but also can help improving models and thus increase flood resilience.
Maurizio Mazzoleni was born in Brescia in November 1986. Mr. Mazzoleni graduated from University of Brescia, in Brescia, Italy, in May 2011. During his university studies he continued to pursue his interest in the flood protection by moving to UNESCO-IHE with the support of a scholarship awarded by University of Brescia to carry out his Master Thesis. Afterwards, he cooperate for 1 year within the KULTURisk Project as research fellow of the University of Brescia. Currently, Mr. Mazzoleni is a PhD candidate at UNESCO-IHE Institute for Water Education under the Department of Integrated Water Systems and Governance, Delft, The Netherlands. His research interest include hydrologic and hydrodynamic modelling, in particular he dealt with issue related to flood forecasting, data assimilation, flood inundation mapping, flood risk and uncertainty analysis, flood defence systems design and reliability analysis, statistical hydrology.
- Autor: Maurizio Mazzoleni
- 2017, 240 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 135164646X
- ISBN-13: 9781351646468
- Erscheinungsdatum: 16.03.2017
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- Dateiformat: ePub
- Grösse: 5.98 MB
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