A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India)

Pham, BT; Pradhan, B; Bui, DT; Prakash, I; Dholakia, MB

A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India)

Pham, BT Pradhan, B

Bui, DT Prakash, I Dholakia, MB

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Environmental Modelling & Software, 2016, 84, pp. 240-250
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Pham, BT
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Bui, DT
dc.contributor.author	Prakash, I
dc.contributor.author	Dholakia, MB
dc.date.accessioned	2022-07-14T22:54:15Z
dc.date.available	2022-07-14T22:54:15Z
dc.date.issued	2016
dc.identifier.citation	Environmental Modelling & Software, 2016, 84, pp. 240-250
dc.identifier.issn	1364-8152
dc.identifier.issn	1873-6726
dc.identifier.uri	http://hdl.handle.net/10453/158929
dc.description.abstract	Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Environmental Modelling & Software
dc.relation.isbasedon	10.1016/j.envsoft.2016.07.005
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Engineering
dc.title	A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India)
dc.type	Journal Article
utslib.citation.volume	84
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-14T22:54:13Z
pubs.publication-status	Published
pubs.volume	84

Abstract:

Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naïve Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910–0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively.

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http://hdl.handle.net/10453/158929