Manifestation of SVM-Based Rectified Linear Unit (ReLU) Kernel Function in Landslide Modelling

Alzuhairi, M; Pradhan, B

Manifestation of SVM-Based Rectified Linear Unit (ReLU) Kernel Function in Landslide Modelling

Alzuhairi, M Pradhan, B

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Space Science and Communication for Sustainability, 2018, pp. 185-195
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Alzuhairi, M
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-12-04T00:03:37Z
dc.date.available	2022-12-04T00:03:37Z
dc.date.issued	2018-11-01
dc.identifier.citation	Space Science and Communication for Sustainability, 2018, pp. 185-195
dc.identifier.isbn	9789811065736
dc.identifier.uri	http://hdl.handle.net/10453/164076
dc.description.abstract	Support vector machines (SVM) are the most popular machine learning methods currently used to model landslides. To model the complex nature of landslides, the SVM model parameters (kernel function, penalty parameter) should be fine-tuned to achieve the best possible accuracy. The main objective of this paper is to evaluate the commonly used rectified linear unit (ReLU) activation function in deep learning for the SVM model as a kernel function. A case study of the Cameron Highlands, located in the Peninsular Malaysia, was selected and a dataset was acquired through the airborne LiDAR system, topographical databases, and SPOT satellite images. The SVM modelling with ReLU kernel was implemented in a Matlab environment. Overall, 11 landslide factors and 81 landslide locations (inventory map) were used. Experimental results showed that the ReLU kernel function could achieve a higher accuracy (0.81) than other kernels when using a lower number of landslide factors. It was found that the ReLU kernel function is more accurate (0.73) than RBF kernel (0.71) when using very limited factors (such as altitude, slope, and curvature). The kernel ReLU could improve the performance of landslide susceptibility modelling with SVM while reducing the need to use large datasets.
dc.format.extent	28
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Space Science and Communication for Sustainability
dc.relation.isbasedon	10.1007/978-981-10-6574-3_16
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Manifestation of SVM-Based Rectified Linear Unit (ReLU) Kernel Function in Landslide Modelling
dc.type	Chapter
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-04T00:03:34Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
dc.location	Switzerland

Abstract:

Support vector machines (SVM) are the most popular machine learning methods currently used to model landslides. To model the complex nature of landslides, the SVM model parameters (kernel function, penalty parameter) should be fine-tuned to achieve the best possible accuracy. The main objective of this paper is to evaluate the commonly used rectified linear unit (ReLU) activation function in deep learning for the SVM model as a kernel function. A case study of the Cameron Highlands, located in the Peninsular Malaysia, was selected and a dataset was acquired through the airborne LiDAR system, topographical databases, and SPOT satellite images. The SVM modelling with ReLU kernel was implemented in a Matlab environment. Overall, 11 landslide factors and 81 landslide locations (inventory map) were used. Experimental results showed that the ReLU kernel function could achieve a higher accuracy (0.81) than other kernels when using a lower number of landslide factors. It was found that the ReLU kernel function is more accurate (0.73) than RBF kernel (0.71) when using very limited factors (such as altitude, slope, and curvature). The kernel ReLU could improve the performance of landslide susceptibility modelling with SVM while reducing the need to use large datasets.

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http://hdl.handle.net/10453/164076