Articles | Volume 17, issue 5
Nat. Hazards Earth Syst. Sci., 17, 735–747, 2017
https://doi.org/10.5194/nhess-17-735-2017
Nat. Hazards Earth Syst. Sci., 17, 735–747, 2017
https://doi.org/10.5194/nhess-17-735-2017
Research article
19 May 2017
Research article | 19 May 2017

Probabilistic flood extent estimates from social media flood observations

Tom Brouwer et al.

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Cited articles

Aronica, G., Bates, P. D., and Horrit, M. S.: Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Brouwer, T.: Twitter Flood Mapping Scripts: First Release [Data set], https://doi.org/10.5281/zenodo.165818, 2016.
Carter, W. N.: Disaster Management: A Disaster Manager's Handbook, Asian Development Bank, Mandaluyong City, Philippines, 2008.
Dullof, J. and Doucette, P.: The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences, Int. J. Geo-Inf., 3, 817–852, https://doi.org/10.3390/ijgi3020817, 2014.
EA (Environment Agency): LIDAR Composite DTM – 2 m, available at: https://data.gov.uk/dataset/lidar-composite-dtm-2m1 (last access: 3 May 2016), 2014.
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Short summary
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.
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