Application of Machine Learning Techniques to Detect and Understand the Impacts of Global Warming on Southeast Australia

Speer, MS; Leslie, LM

Application of Machine Learning Techniques to Detect and Understand the Impacts of Global Warming on Southeast Australia

Speer, MS Leslie, LM

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Publisher:: Project MUSE
Publication Type:: Journal Article
Citation:: Georgetown Journal of International Affairs, 2023, 24, (2), pp. 260-266
Issue Date:: 2023-09

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Field	Value	Language
dc.contributor.author	Speer, MS
dc.contributor.author	Leslie, LM
dc.date.accessioned	2023-12-03T05:37:28Z
dc.date.available	2023-12-03T05:37:28Z
dc.date.issued	2023-09
dc.identifier.citation	Georgetown Journal of International Affairs, 2023, 24, (2), pp. 260-266
dc.identifier.issn	2471-8831
dc.identifier.uri	http://hdl.handle.net/10453/173644
dc.description.abstract	<jats:p xml:lang="en"> Abstract: Australia is adversely affected by global warming (GW), as its well-known cycles of droughts, floods, and extreme weather events are increasingly amplified by GW. Here, the focus is the impacts of GW on populous southeast Australia. Machine Learning attribution techniques have been applied to identify the main drivers of these impacts. This article presents the detection examples of the most relevant drivers, individually and in combination, responsible for observed trends in precipitation and temperature due to GW.</jats:p>
dc.language	en
dc.publisher	Project MUSE
dc.relation.ispartof	Georgetown Journal of International Affairs
dc.relation.isbasedon	10.1353/gia.2023.a913654
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1606 Political Science, 1801 Law
dc.subject.classification	4408 Political science
dc.subject.classification	4803 International and comparative law
dc.title	Application of Machine Learning Techniques to Detect and Understand the Impacts of Global Warming on Southeast Australia
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	1606 Political Science
utslib.for	1801 Law
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2023-12-03T05:37:13Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	2

Abstract:

Abstract: Australia is adversely affected by global warming (GW), as its well-known cycles of droughts, floods, and extreme weather events are increasingly amplified by GW. Here, the focus is the impacts of GW on populous southeast Australia. Machine Learning attribution techniques have been applied to identify the main drivers of these impacts. This article presents the detection examples of the most relevant drivers, individually and in combination, responsible for observed trends in precipitation and temperature due to GW.

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http://hdl.handle.net/10453/173644