Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

Paryani, S; Neshat, A; Pradhan, B

Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

Paryani, S Neshat, A Pradhan, B

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: The Egyptian Journal of Remote Sensing and Space Sciences, 2021, 24, (3), pp. 845-855
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Paryani, S
dc.contributor.author	Neshat, A
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-02-21T01:43:54Z
dc.date.available	2022-02-21T01:43:54Z
dc.date.issued	2021-12-01
dc.identifier.citation	The Egyptian Journal of Remote Sensing and Space Sciences, 2021, 24, (3), pp. 845-855
dc.identifier.issn	1110-9823
dc.identifier.issn	2090-2476
dc.identifier.uri	http://hdl.handle.net/10453/154713
dc.description.abstract	Landslide is a natural phenomenon that can turn into a natural disaster. The main goal of this research was to spatial prediction of a high-risk region located in the Zagros mountains, Iran, using hybrid machine learning and metaheuristic algorithms, namely the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), the Harris hawks optimisation (HHO), and the bat algorithm (BA). The landslide occurrences were first divided into training and testing datasets with a 70/30 ratio. Fourteen landslide-related factors were considered, and the stepwise weight assessment ratio analysis (SWARA) were employed to determine the correlation between landslides and factors. After that, the hybrid models of ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were applied to generate landslide susceptibility maps (LSMs). Finally, in order to validation and comparison of the applied models, two indexes, namely mean square error (MSE) and area under the ROC curve (AUROC), were used. According to the validation results, the AUROC values for the ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were 0.849, 0.82, 0.895, and 0.865, respectively. The SVR-HHO showed the highest accuracy, with AUROC of 0.895 and lowest MSE of 0.147, and ANFIS-BA showed the least accuracy with an AUROC value of 0.82 and MSE value of 0.218. Based on the results, although four hybrid models with more than 80% accuracy can generate very good results, the SVR is superior to the ANFIS model, whereas the HHO algorithm outperformed the bat algorithm. The map generated in this study can be employed by land use planners in more efficient management.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	The Egyptian Journal of Remote Sensing and Space Sciences
dc.relation.isbasedon	10.1016/j.ejrs.2021.08.006
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms
dc.type	Journal Article
utslib.citation.volume	24
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-21T01:43:51Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	3

Abstract:

Landslide is a natural phenomenon that can turn into a natural disaster. The main goal of this research was to spatial prediction of a high-risk region located in the Zagros mountains, Iran, using hybrid machine learning and metaheuristic algorithms, namely the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), the Harris hawks optimisation (HHO), and the bat algorithm (BA). The landslide occurrences were first divided into training and testing datasets with a 70/30 ratio. Fourteen landslide-related factors were considered, and the stepwise weight assessment ratio analysis (SWARA) were employed to determine the correlation between landslides and factors. After that, the hybrid models of ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were applied to generate landslide susceptibility maps (LSMs). Finally, in order to validation and comparison of the applied models, two indexes, namely mean square error (MSE) and area under the ROC curve (AUROC), were used. According to the validation results, the AUROC values for the ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were 0.849, 0.82, 0.895, and 0.865, respectively. The SVR-HHO showed the highest accuracy, with AUROC of 0.895 and lowest MSE of 0.147, and ANFIS-BA showed the least accuracy with an AUROC value of 0.82 and MSE value of 0.218. Based on the results, although four hybrid models with more than 80% accuracy can generate very good results, the SVR is superior to the ANFIS model, whereas the HHO algorithm outperformed the bat algorithm. The map generated in this study can be employed by land use planners in more efficient management.

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