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ICT for Smart Water Systems: Measurements and Data Science pp 209–234Cite as

Exploring Assimilation of Crowdsourcing Observations into Flood Models

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Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 102))

Abstract

This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan and space-time coverage as well as their spatial distribution. The main part of the chapter is dedicated to the optimal assimilation of heterogeneous intermittent data within hydrological and hydraulic models. These approaches are designed to account for the intrinsic uncertainty contained into hydrological observations and model structure, states and parameters. Two case studies, the Brue and Bacchiglione catchments, are considered. Finally, the evaluation of the developed methods is provided. This study demonstrates that networks of low-cost static and dynamic social sensors can complement traditional networks of static physical sensors, for the purpose of improving flood forecasting accuracy. 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 improve models and increase flood resilience.

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Acknowledgements

This research was funded in the framework of the European FP7 Project WeSenseIt: Citizen Observatory of Water, grant agreement No. 308429.

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Authors and Affiliations

  1. Department of Earth Sciences, Uppsala University, Uppsala, Sweden

    M. Mazzoleni

  2. Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden

    M. Mazzoleni

  3. IHE Delft Institute for Water Education, Delft, The Netherlands

    Leonardo Alfonso & D. P. Solomatine

  4. Delft University of Technology, Delft, The Netherlands

    D. P. Solomatine

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  1. M. Mazzoleni
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  2. Leonardo Alfonso
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  3. D. P. Solomatine
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Correspondence to M. Mazzoleni .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Institute of Information Science and Technologies (CNR-ISTI), Pisa, Italy

    Andrea Scozzari

  2. Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK

    Steve Mounce

  3. Department of Civil Engineering, University of Bristol, Bristol, UK

    Dawei Han

  4. National Research Council of Italy, Institute for Electromagnetic Sensing of the Environment (CNR-IREA), Napoli, Italy

    Francesco Soldovieri

  5. Hydroinformatics Chair, IHE Institute for Water Education and Water Resources Section, Delft University of Technology, Delft, The Netherlands

    Dimitri Solomatine

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Mazzoleni, M., Alfonso, L., Solomatine, D.P. (2019). Exploring Assimilation of Crowdsourcing Observations into Flood Models. In: Scozzari, A., Mounce, S., Han, D., Soldovieri, F., Solomatine, D. (eds) ICT for Smart Water Systems: Measurements and Data Science. The Handbook of Environmental Chemistry, vol 102. Springer, Cham. https://doi.org/10.1007/698_2019_403

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