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dc.contributor.authorAnsell, L
dc.contributor.authorDalla Valle, Luciana
dc.date.accessioned2021-04-16T08:34:41Z
dc.date.available2021-04-16T08:34:41Z
dc.date.issued2021-04-05
dc.identifier.urihttp://hdl.handle.net/10026.1/17049
dc.description.abstract

Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations in the South cost of the UK. The results show that our approach, based on the integration of social media data, outperforms traditional methods, providing a more accurate evaluation and prediction of flood events.

dc.language.isoen
dc.subjectstat.AP
dc.subjectstat.AP
dc.titleSocial Media Integration of Flood Data: A Vine Copula-Based Approach
dc.typejournal-article
plymouth.author-urlhttp://arxiv.org/abs/2104.01869v1
plymouth.publisher-urlhttps://arxiv.org/abs/2104.01869v1
plymouth.journalArxiv.org
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2021-04-05
dc.rights.embargodate2021-4-27
dc.rights.embargoperiodNot known
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-04-05
rioxxterms.typeJournal Article/Review
plymouth.funderEnvironmental Futures & Big Data Impact Lab::European Regional Development Fund


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