A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

Fanos, AM; Pradhan, B; Mansor, S; Yusoff, ZM; Abdullah, AFB

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

Fanos, AM Pradhan, B

Mansor, S Yusoff, ZM Abdullah, AFB

Permalink

Publication Type:: Journal Article
Citation:: Landslides, 2018, 15 (9), pp. 1833 - 1850
Issue Date:: 2018-09-01

Closed Access

	Filename	Description	Size
	Fanos2018_Article_AHybridModelUsingMachineLearni.pdf	Published Version	8.06 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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.contributor.author	Mansor, S	en_US
dc.contributor.author	Yusoff, ZM	en_US
dc.contributor.author	Abdullah, AFB	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Landslides, 2018, 15 (9), pp. 1833 - 1850	en_US
dc.identifier.issn	1612-510X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128110
dc.description.abstract	© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.	en_US
dc.relation.ispartof	Landslides	en_US
dc.relation.isbasedon	10.1007/s10346-018-0990-4	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	15	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	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.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.

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

http://hdl.handle.net/10453/128110