Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework
TLDR
In this paper, a real-time modelling framework is presented to identify areas likely to have flooded using data obtained only through social media, using graphics processing unit (GPU) accelerated hydrodynamic modelling.Abstract:
The utility of social media for both collecting and disseminating information during natural disasters is increasingly recognised. The rapid nature of urban flooding from intense rainfall means accurate surveying of peak depths and flood extents is rarely achievable, hindering the validation of urban flood models. This paper presents a real-time modelling framework to identify areas likely to have flooded using data obtained only through social media. Graphics processing unit (GPU) accelerated hydrodynamic modelling is used to simulate flooding in a 48-km2 area of Newcastle upon Tyne, with results automatically compared against flooding identified through social media, allowing inundation to be inferred elsewhere in the city with increased detail and accuracy. Data from Twitter during two 2012 flood events are used to test the framework, with the inundation results indicative of good agreement against crowd-sourced and anecdotal data, even though the sample of successfully geocoded Tweets was relatively small.read more
Citations
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What Is Nuisance Flooding? Defining and Monitoring an Emerging Challenge
TL;DR: In this article, a simple quantitative definition of nuisance flooding is proposed based on established flood intensity thresholds for flood consequences (e.g., pedestrian safety, property damage, and health risks).
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Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation
TL;DR: In this article, community-based rainfall, river level and flood observations have been successfully collected and quality-checked, and used to build and run a physically-based, spatially-distributed catchment model, SHETRAN.
Journal ArticleDOI
Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm
TL;DR: Wang et al. as mentioned in this paper developed a framework to assess disaster impacts with social media data, and examined the potential of information extraction with tweets from Weibo platform to inform disaster response and recovery in China, using the case of 2016 Wuhan rainstorm and flood disaster.
Journal ArticleDOI
Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions
Feifei Zheng,Ruoling Tao,Holger R. Maier,Holger R. Maier,Holger R. Maier,Linda See,Dragan Savic,Tuqiao Zhang,Qiuwen Chen,Thaine H. Assumpção,Pan Yang,Pan Yang,Bardia Heidari,Jörg Rieckermann,Barbara S. Minsker,Weiwei Bi,Ximing Cai,Dimitri Solomatine,Ioana Popescu +18 more
TL;DR: A review of the state of the art in this field can be found in this article, where the authors present a framework for categorizing the methods used in the seven domains of geophysics considered in this review.
Journal ArticleDOI
Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects.
N. Njue,J. Stenfert Kroese,Jan Gräf,Suzanne Jacobs,Björn Weeser,Lutz Breuer,Mariana C. Rufino +6 more
TL;DR: A comprehensive review on citizen science and crowdsourced data collection within the context of hydrology, based on a synthesis of 71 articles from 2001 to 2018, is provided in this article, where participation mostly comprises a contributory citizen science model, which engages citizens in data collection.
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