Abstract
AbstractThe 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.
Highlights
The UK was subjected to a series of intense storms throughout 2012, bringing severe flooding and damage totalling millions of pounds
Each simulation runs only to the 450-s interval while the event is in progress, and only models where suitable criteria were identified from social media are scheduled for execution; this means that even though multiple model runs are required to find an appropriate match, these can often be achieved in near real-time
The integrated modelling framework was tested using retrospective data collected from Twitter following the two major flood events in Newcastle upon Tyne during 2012, the smaller of the two occurring on August 5 and the larger on June 28
Summary
The UK was subjected to a series of intense storms throughout 2012, bringing severe flooding and damage totalling millions of pounds. These messages are processed in order to identify criteria against which model runs can be assessed, thereby finding a suitable hydrodynamic model of the flood event and creating a simulation that closely represents the reported inundation within the city.
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