Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR Data

Lay, US; Pradhan, B; Yusoff, ZBM; Abdallah, AFB; Aryal, J; Park, HJ

Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR Data

Lay, US

Pradhan, B

Yusoff, ZBM Abdallah, AFB Aryal, J

Park, HJ

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Publication Type:: Journal Article
Citation:: Sensors (Switzerland), 2019, 19 (16)
Issue Date:: 2019-08-02

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Field	Value	Language
dc.contributor.author	Lay, US https://orcid.org/0000-0002-9591-7879	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Yusoff, ZBM	en_US
dc.contributor.author	Abdallah, AFB	en_US
dc.contributor.author	Aryal, J https://orcid.org/0000-0002-4875-2127	en_US
dc.contributor.author	Park, HJ	en_US
dc.date.available	2019-07-22	en_US
dc.date.issued	2019-08-02	en_US
dc.identifier.citation	Sensors (Switzerland), 2019, 19 (16)	en_US
dc.identifier.issn	1424-8220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135608
dc.description.abstract	© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.	en_US
dc.relation.ispartof	Sensors (Switzerland)	en_US
dc.relation.isbasedon	10.3390/s19163451	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Analytical Chemistry	en_US
dc.title	Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR Data	en_US
dc.type	Journal Article
utslib.citation.volume	16	en_US
utslib.citation.volume	19	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0301 Analytical Chemistry	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	0602 Ecology	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.issue	16	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

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http://hdl.handle.net/10453/135608