Probabilistic flood extent estimates from social media flood observations
| Autor: | Dirk Eilander, Arnejan van Loenen, Kathelijne Mariken Wijnberg, Jurjen Wagemaker, Jan Verkade, Tom Brouwer, Martijn J. Booij |
|---|---|
| Přispěvatelé: | Water and Climate Risk, Water Management, Marine and Fluvial Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology Flood forecasting 0211 other engineering and technologies Climate change 02 engineering and technology 01 natural sciences Flood stage lcsh:TD1-1066 100-year flood SDG 13 - Climate Action lcsh:Environmental technology. Sanitary engineering lcsh:Environmental sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences Hydrology lcsh:GE1-350 Flood myth Uncertain data Flooding (psychology) lcsh:QE1-996.5 Probabilistic logic lcsh:Geography. Anthropology. Recreation lcsh:Geology lcsh:G General Earth and Planetary Sciences Environmental science |
| Zdroj: | Natural Hazards and Earth System Sciences, 17(5), 735-747. European Geosciences Union Natural hazards and earth system sciences, 17(5), 735-747. European Geosciences Union Natural Hazards and Earth System Sciences, 735-747 STARTPAGE=735;ENDPAGE=747;TITLE=Natural Hazards and Earth System Sciences Natural Hazards and Earth System Sciences, Vol 17, Iss 5, Pp 735-747 (2017) Brouwer, T, Eilander, D, Van Loenen, A, Booij, M J, Wijnberg, K M, Verkade, J S & Wagemaker, J 2017, ' Probabilistic flood extent estimates from social media flood observations ', Natural Hazards and Earth System Sciences, vol. 17, no. 5, pp. 735-747 . https://doi.org/10.5194/nhess-17-735-2017 |
| ISSN: | 1561-8633 1684-9981 |
| DOI: | 10.5194/nhess-17-735-2017 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|