A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS

Fanos, AM; Pradhan, B

A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS

Fanos, AM Pradhan, B

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Publication Type:: Journal Article
Citation:: Catena, 2019, 172 pp. 435 - 450
Issue Date:: 2019-01-01

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	Fanos and Pradhan_CATENA_2019_Full Citation.pdf	Published Version	11.98 MB	Adobe PDF	View/Open
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Field	Value	Language
dc.contributor.author	Fanos, AM	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Catena, 2019, 172 pp. 435 - 450	en_US
dc.identifier.issn	0341-8162	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130801
dc.description.abstract	© 2018 Elsevier B.V. Rockfall hazards occur widely in regions with steep terrain such as Kinta Valley, Malaysia. Rockfalls threaten urban areas and the transportation corridors that pass through such areas. This paper proposes a comprehensive rockfall hazard assessment strategy based on high-resolution laser scanning data (LiDAR), both airborne and terrestrial. It provides (1) rockfall source identification by developing a hybrid model based on a bagging neural network (BBNN), which is compared with various machine learning algorithms and ensemble models (bagging, boosting, voting) and a Gaussian mixture model; (2) 3D modelling of rockfall kinematic processes (trajectory distribution, frequency, velocity, kinetic energy, bounce height, impact location); and (3) hazard zonation based on spatial modelling in combination with an analytical hierarchy process (AHP) in a geographic information system (GIS). In addition, mitigation measures are suggested based on the modelling results. The proposed methodology was validated in three study areas to test the applicability and generalisability of the methods. The results show that the proposed hybrid model can accurately identify rockfall source areas at the regional scale. It achieved a 97% training accuracy and 5-fold cross-validation area under curve (AUC) value of 0.96. The mechanical parameters of the developed 3D model were calibrated with an accuracy of 97%, 93% and 95% for Gunung Lang, Gua Tambun and Gunung Rapat areas, respectively. In addition, the proposed spatial model effectively delineates areas at risk of rockfalls. This method provides a comprehensive understanding of rockfall hazards that can assist authorities to develop proper management and protection of urban areas and transportation corridors.	en_US
dc.relation.ispartof	Catena	en_US
dc.relation.isbasedon	10.1016/j.catena.2018.09.012	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Geochemistry & Geophysics	en_US
dc.title	A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS	en_US
dc.type	Journal Article
utslib.citation.volume	172	en_US
utslib.for	0403 Geology	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0503 Soil Sciences	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 - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	172	en_US

Abstract:

© 2018 Elsevier B.V. Rockfall hazards occur widely in regions with steep terrain such as Kinta Valley, Malaysia. Rockfalls threaten urban areas and the transportation corridors that pass through such areas. This paper proposes a comprehensive rockfall hazard assessment strategy based on high-resolution laser scanning data (LiDAR), both airborne and terrestrial. It provides (1) rockfall source identification by developing a hybrid model based on a bagging neural network (BBNN), which is compared with various machine learning algorithms and ensemble models (bagging, boosting, voting) and a Gaussian mixture model; (2) 3D modelling of rockfall kinematic processes (trajectory distribution, frequency, velocity, kinetic energy, bounce height, impact location); and (3) hazard zonation based on spatial modelling in combination with an analytical hierarchy process (AHP) in a geographic information system (GIS). In addition, mitigation measures are suggested based on the modelling results. The proposed methodology was validated in three study areas to test the applicability and generalisability of the methods. The results show that the proposed hybrid model can accurately identify rockfall source areas at the regional scale. It achieved a 97% training accuracy and 5-fold cross-validation area under curve (AUC) value of 0.96. The mechanical parameters of the developed 3D model were calibrated with an accuracy of 97%, 93% and 95% for Gunung Lang, Gua Tambun and Gunung Rapat areas, respectively. In addition, the proposed spatial model effectively delineates areas at risk of rockfalls. This method provides a comprehensive understanding of rockfall hazards that can assist authorities to develop proper management and protection of urban areas and transportation corridors.

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http://hdl.handle.net/10453/130801