Forest fire induced Natech risk assessment: A survey of geospatial technologies

Naderpour, M; Rizeei, HM; Khakzad, N; Pradhan, B

Forest fire induced Natech risk assessment: A survey of geospatial technologies

Naderpour, M

Rizeei, HM

Khakzad, N

Pradhan, B

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Publication Type:: Journal Article
Citation:: Reliability Engineering and System Safety, 2019, 191
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Rizeei, HM https://orcid.org/0000-0002-7690-8440	en_US
dc.contributor.author	Khakzad, N https://orcid.org/0000-0002-3899-6830	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	Reliability Engineering and System Safety, 2019, 191	en_US
dc.identifier.issn	0951-8320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134365
dc.description.abstract	© 2019 Elsevier Ltd Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.	en_US
dc.relation.ispartof	Reliability Engineering and System Safety	en_US
dc.relation.isbasedon	10.1016/j.ress.2019.106558	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	Forest fire induced Natech risk assessment: A survey of geospatial technologies	en_US
dc.type	Journal Article
utslib.citation.volume	191	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	1503 Business and Management	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	15 Commerce, Management, Tourism and Services	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-11-01T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	191	en_US

Abstract:

© 2019 Elsevier Ltd Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.

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http://hdl.handle.net/10453/134365