Articles | Volume 15, issue 12
https://doi.org/10.5194/nhess-15-2725-2015
https://doi.org/10.5194/nhess-15-2725-2015
Research article
 | 
21 Dec 2015
Research article |  | 21 Dec 2015

Social media as an information source for rapid flood inundation mapping

J. Fohringer, D. Dransch, H. Kreibich, and K. Schröter

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Cited articles

Abel, F., Hauff, C., Houben, G.-J., Stronkman, R., and Tao, K.: Semantics + filtering + search = twitcident exploring information in social web streams, in: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, HT '12, ACM, New York, NY, USA, 285–294, https://doi.org/10.1145/2309996.2310043, 2012.
Apel, H., Aronica, G., Kreibich, H., and Thieken, A.: Flood risk analyses – how detailed do we need to be?, Nat. Hazards, 49, 79–98, https://doi.org/10.1007/s11069-008-9277-8, 2009.
Bates, P. D.: Integrating remote sensing data with flood inundation models: how far have we got?, Hydrol. Process., 26, 2515–2521, https://doi.org/10.1002/hyp.9374, 2012.
Di Baldassarre, G., Schumann, G., and Bates, P.: Near real time satellite imagery to support and verify timely flood modelling, Hydrol. Process., 23, 799–803, https://doi.org/10.1002/hyp.7229, 2009.
Dransch, D., Poser, K., Fohringer, J., and Lucas, C.: Volunteered geographic information for disaster management, in: Citizen E-Participation in Urban Governance: Crowdsourcing and Collaborative Creativity, Advances in electronic government, digital divide, and regional development (AEGDDRD) book series, edited by: Silva, C. N., Information Science Reference, Hershey, Pa., 98–118, 2013.
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Short summary
During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The case study of the June 2013 flood in the city of Dresden shows that social media may help to bridge the information gap when traditional data sources are lacking or are sparse.
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