Decision Briefing Generation on Meteorological Social Events

Shi, Kaize

Decision Briefing Generation on Meteorological Social Events

Shi, Kaize

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Shi, Kaize
dc.date.accessioned	2023-03-17T01:07:51Z
dc.date.available	2023-03-17T01:07:51Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/167399
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Meteorological disasters have the characteristics of suddenness, complexity, dynamics, and diversity, which have inflicted severe challenges on emergency decision support services in disaster environments. Social sensors collect data from mobile devices worn or carried by humans, intuitively perceive the environmental conditions associated with a meteorological disaster through human senses, and have the advantages of comprehensive coverage, communication in real-time, and low cost. Decision briefing is an effective support mode of emergency management services for sudden meteorological disasters, and it is also the primary way to deliver meteorological decision knowledge. This thesis focuses on the critical technologies for the automatic generation of meteorological event knowledge-enhanced decision briefing based on social sensor signals, proposing the co-occurrence feature-based sudden meteorological event detect model, multiple knowledge-enhanced meteorological decision briefing generation model, and meteorological event knowledge-enhanced decision briefing optimization model. In addition, this thesis reports a prototype application of the meteorological decision briefing integrated with the above research foci, which showcases the use of social sensor signals to provide feedback on daily meteorological events and then provides decision support services based on meteorological decision briefing.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/167399/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2022 Kaize Shi
dc.rights	au.edu.uts.lib/ppc
dc.title	Decision Briefing Generation on Meteorological Social Events	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Meteorological disasters have the characteristics of suddenness, complexity, dynamics, and diversity, which have inflicted severe challenges on emergency decision support services in disaster environments. Social sensors collect data from mobile devices worn or carried by humans, intuitively perceive the environmental conditions associated with a meteorological disaster through human senses, and have the advantages of comprehensive coverage, communication in real-time, and low cost. Decision briefing is an effective support mode of emergency management services for sudden meteorological disasters, and it is also the primary way to deliver meteorological decision knowledge. This thesis focuses on the critical technologies for the automatic generation of meteorological event knowledge-enhanced decision briefing based on social sensor signals, proposing the co-occurrence feature-based sudden meteorological event detect model, multiple knowledge-enhanced meteorological decision briefing generation model, and meteorological event knowledge-enhanced decision briefing optimization model. In addition, this thesis reports a prototype application of the meteorological decision briefing integrated with the above research foci, which showcases the use of social sensor signals to provide feedback on daily meteorological events and then provides decision support services based on meteorological decision briefing.

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http://hdl.handle.net/10453/167399