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Lookup NU author(s): Dr Luke Smith,
Professor Qiuhua Liang,
Professor Philip James,
Dr Wen Lin
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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 48km2 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 is 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.
Author(s): Smith L, Liang Q, James P, Lin W
Publication type: Article
Publication status: Published
Journal: Journal of Flood Risk Management
Print publication date: 01/09/2017
Online publication date: 27/03/2015
Acceptance date: 27/12/2014
Date deposited: 30/03/2015
ISSN (electronic): 1753-318X
Publisher: Wiley-Blackwell Publishing Ltd.
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