Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India

Saha, S; Roy, J; Pradhan, B; Hembram, TK

Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India

Saha, S Roy, J Pradhan, B

Hembram, TK

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Advances in Space Research, 2021, 68, (7), pp. 2819-2840
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Saha, S
dc.contributor.author	Roy, J
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Hembram, TK
dc.date.accessioned	2022-02-21T01:19:45Z
dc.date.available	2022-02-21T01:19:45Z
dc.date.issued	2021-10-01
dc.identifier.citation	Advances in Space Research, 2021, 68, (7), pp. 2819-2840
dc.identifier.issn	0273-1177
dc.identifier.issn	1879-1948
dc.identifier.uri	http://hdl.handle.net/10453/154711
dc.description.abstract	Landslide is a big problem in the mountainous region all over the world. Sikkim Himalayan region is also suffering from landslide problem. This study's main objective was to generate landslide susceptibility map (LSM) considering the hybrid ensemble of machine learning approaches using different sample ratios. Random Forest (RF) as the base classifier an ensemble with bagging, Rotation Forest (RTF), and Random Subspace (RS) Meta classifiers were used for spatial landslide modeling. First, collected 86 landslides locations through field investigation and from Sikkim district disaster office were mapped as a landslide inventory. Collected landslide locations were categorized into training and testing datasets randomly using four sample ratios (50:50, 60:40, 70:30 and 80:20). Based on the four sampling ratios and fifteen conditioning factors, a total of sixteen LSMs were prepared using RF, Bagging-RF (B-RF), RTF-RF and RS-RF in GIS platform. For assessing the modeling accuracy and comparison among these, the area under the receiver operating characteristics (AUROC) and other statistical methods such as root-mean-square-error (RMSE), mean-absolute-error (MAE) and R-index methods were used. The overall proficiency of RS-RF (AUC = 0.871, 0.847 of 50%:50%, AUC = 0.925, 0.931 of 60%:40%, AUC = 0.933, 0.939 of 70%:30%; AUC = 0.927, 0.933 of 80%:20%) was found to be substantially greater than the results of RF, B-RF, and RTF-RF. The RS-RF model and 70:30 sample ratio had the highest goodness-of-fit and accuracy as per the RMSE, MAE, and R-index methods. Furthermore, the model based on RS-RF is a promising and acceptable way of mapping regional landslides.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Advances in Space Research
dc.relation.isbasedon	10.1016/j.asr.2021.05.018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0201 Astronomical and Space Sciences, 0901 Aerospace Engineering, 0913 Mechanical Engineering
dc.subject.classification	Aerospace & Aeronautics
dc.title	Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India
dc.type	Journal Article
utslib.citation.volume	68
utslib.for	0201 Astronomical and Space Sciences
utslib.for	0901 Aerospace Engineering
utslib.for	0913 Mechanical Engineering
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	*
pubs.consider-herdc	false
dc.date.updated	2022-02-21T01:19:38Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	68
utslib.citation.issue	7

Abstract:

Landslide is a big problem in the mountainous region all over the world. Sikkim Himalayan region is also suffering from landslide problem. This study's main objective was to generate landslide susceptibility map (LSM) considering the hybrid ensemble of machine learning approaches using different sample ratios. Random Forest (RF) as the base classifier an ensemble with bagging, Rotation Forest (RTF), and Random Subspace (RS) Meta classifiers were used for spatial landslide modeling. First, collected 86 landslides locations through field investigation and from Sikkim district disaster office were mapped as a landslide inventory. Collected landslide locations were categorized into training and testing datasets randomly using four sample ratios (50:50, 60:40, 70:30 and 80:20). Based on the four sampling ratios and fifteen conditioning factors, a total of sixteen LSMs were prepared using RF, Bagging-RF (B-RF), RTF-RF and RS-RF in GIS platform. For assessing the modeling accuracy and comparison among these, the area under the receiver operating characteristics (AUROC) and other statistical methods such as root-mean-square-error (RMSE), mean-absolute-error (MAE) and R-index methods were used. The overall proficiency of RS-RF (AUC = 0.871, 0.847 of 50%:50%, AUC = 0.925, 0.931 of 60%:40%, AUC = 0.933, 0.939 of 70%:30%; AUC = 0.927, 0.933 of 80%:20%) was found to be substantially greater than the results of RF, B-RF, and RTF-RF. The RS-RF model and 70:30 sample ratio had the highest goodness-of-fit and accuracy as per the RMSE, MAE, and R-index methods. Furthermore, the model based on RS-RF is a promising and acceptable way of mapping regional landslides.

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