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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 5 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 735-747, 2017
https://doi.org/10.5194/nhess-17-735-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 May 2017

Research article | 19 May 2017

Probabilistic flood extent estimates from social media flood observations

Tom Brouwer1,2, Dirk Eilander1, Arnejan van Loenen1, Martijn J. Booij2, Kathelijne M. Wijnberg2, Jan S. Verkade1, and Jurjen Wagemaker3 Tom Brouwer et al.
  • 1Deltares, Delft, Boussinesqweg 1, 2629 HV, the Netherlands
  • 2Dept. of Water Engineering and Management, University of Twente, Enschede, Drienerlolaan 5, 7522NB, the Netherlands
  • 3FloodTags, The Hague, Binckhorstlaan 36, 2511 BE, the Netherlands

Abstract. The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F(2) = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.

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The increasing number and severity of floods, driven by e.g. urbanization, subsidence and climate change, create a growing need for accurate and timely flood maps. At the same time social media is a source of much real-time data that is still largely untapped in flood disaster management. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.
The increasing number and severity of floods, driven by e.g. urbanization, subsidence and...
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