Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/nhess-2016-376
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
25 Nov 2016
Review status
A revision of this discussion paper was accepted for the journal Natural Hazards and Earth System Sciences (NHESS) and is expected to appear here in due course.
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 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, creates a growing need for accurate and timely flood maps. This research focussed on creating flood maps using user generated content from Twitter. Twitter data has added value over traditional methods such as remote sensing and hydraulic models, since the data is available almost instantly, in contrast to remote sensing and requires less detail than hydraulic models. Deterministic flood maps created using these data showed good performance (F(2) = 0.69) for a case study in York (UK). For York the main source of uncertainty in the probabilistic flood maps was found to be the error of the locations derived from the Twitter data. Errors in the elevation data and parameters of the applied algorithm contributed less to flood extent uncertainty. Although the generated probabilistic 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.

Citation: Brouwer, T., Eilander, D., van Loenen, A., Booij, M. J., Wijnberg, K. M., Verkade, J. S., and Wagemaker, J.: Probabilistic Flood Extent Estimates from Social Media Flood Observations, Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2016-376, in review, 2016.
Tom Brouwer et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Referee comment', Anonymous Referee #1, 26 Dec 2016 Printer-friendly Version 
AC1: 'response to RC1', Dirk Eilander, 03 Mar 2017 Printer-friendly Version Supplement 
 
RC2: 'A rigorous method and useful application case', Anonymous Referee #2, 19 Jan 2017 Printer-friendly Version 
AC2: 'response to RC2', Dirk Eilander, 03 Mar 2017 Printer-friendly Version Supplement 
Tom Brouwer et al.

Data sets

Twitter Flood Mapping Scripts: First Release
T. Brouwer, D. Eilander, and J. Wagemaker
doi:10.5281/zenodo.165818
Tom Brouwer et al.

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Short summary
The increasing number and severity of floods, driven by e.g. urbanization, subsidence and climate change, creates a growing need for accurate and timely flood maps. At the same time social media are a source of big- and 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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