Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review

Ogie, RI; Castilla Rho, J; Clarke, RJ

Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review

Ogie, RI Castilla Rho, J

Clarke, RJ

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: https://ieeexplore.ieee.org/xpl/conhome/8634895/proceeding, 2019
Issue Date:: 2019-02-07

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Field	Value	Language
dc.contributor.author	Ogie, RI	en_US
dc.contributor.author	Castilla Rho, J https://orcid.org/0000-0002-8468-8636	en_US
dc.contributor.author	Clarke, RJ	en_US
dc.date	2018-12-04	en_US
dc.date.issued	2019-02-07	en_US
dc.identifier.citation	https://ieeexplore.ieee.org/xpl/conhome/8634895/proceeding, 2019	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137669
dc.description.abstract	Effective communication of disaster risks is crucial to provoking appropriate responses from citizens and emergency operators. With recent advancement in Artificial Intelligence (AI), several researchers have begun exploring machine learning techniques in improving disaster risk communication. This paper adopts a systematic literature approach to report on the various research activities involving the application of AI in disaster risk communication. The study found that research activities focus on two broad areas: (1) prediction and monitoring for early warning, and (2) information extraction and classification for situational awareness. These broad areas are discussed, including background information to help establish future applications of AI in disaster risk communication. The paper concludes with recommendations of several ways in which AI applications can have a broader role in disaster risk communication.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	https://ieeexplore.ieee.org/xpl/conhome/8634895/proceeding	en_US
dc.relation.ispartof	2018 5th International Conference on Information and Communication Technologies for Disaster Management	en_US
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Artificial Intelligence in Disaster Risk Communication: A Systematic Literature Review	en_US
dc.type	Conference Proceeding
utslib.location.activity	Sendai, Japan	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false	en_US
pubs.finish-date	2018-12-07	en_US
pubs.publication-status	Published online	en_US
pubs.start-date	2018-12-04	en_US

Abstract:

Effective communication of disaster risks is crucial to provoking appropriate responses from citizens and emergency operators. With recent advancement in Artificial Intelligence (AI), several researchers have begun exploring machine learning techniques in improving disaster risk communication. This paper adopts a systematic literature approach to report on the various research activities involving the application of AI in disaster risk communication. The study found that research activities focus on two broad areas: (1) prediction and monitoring for early warning, and (2) information extraction and classification for situational awareness. These broad areas are discussed, including background information to help establish future applications of AI in disaster risk communication. The paper concludes with recommendations of several ways in which AI applications can have a broader role in disaster risk communication.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/137669