An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

Mezaal, MR; Pradhan, B

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

Mezaal, MR Pradhan, B

Permalink

Publication Type:: Journal Article
Citation:: Catena, 2018, 167 pp. 147 - 159
Issue Date:: 2018-08-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (769.24 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Mezaal, MR	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.available	2020-08-28T19:04:49Z
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	Catena, 2018, 167 pp. 147 - 159	en_US
dc.identifier.issn	0341-8162	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130002
dc.description.abstract	© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia.	en_US
dc.relation.ispartof	Catena	en_US
dc.relation.isbasedon	10.1016/j.catena.2018.04.038	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Geochemistry & Geophysics	en_US
dc.title	An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data	en_US
dc.type	Journal Article
utslib.citation.volume	167	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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	167	en_US

Abstract:

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia.

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

http://hdl.handle.net/10453/130002