Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Dikshit, A; Sarkar, R; Pradhan, B; Jena, R; Drukpa, D; Alamri, AM

Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Dikshit, A

Sarkar, R Pradhan, B

Jena, R Drukpa, D Alamri, AM

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Water, 2020, 12 (1), pp. 267 - 267
Issue Date:: 2020-01-17

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Field	Value	Language
dc.contributor.author	Dikshit, A https://orcid.org/0000-0003-2876-4080	en_US
dc.contributor.author	Sarkar, R	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Jena, R	en_US
dc.contributor.author	Drukpa, D	en_US
dc.contributor.author	Alamri, AM	en_US
dc.date.accessioned	2020-04-06T11:43:11Z
dc.date.available	2020-04-06T11:43:11Z
dc.date.issued	2020-01-17	en_US
dc.identifier.citation	Water, 2020, 12 (1), pp. 267 - 267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139851
dc.description.abstract	<jats:p>Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.</jats:p>	en_US
dc.language	en	en_US
dc.publisher	MDPI AG	en_US
dc.relation.ispartof	Water	en_US
dc.relation.isbasedon	10.3390/w12010267	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	12	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.publication-status	Published online	en_US
pubs.volume	12	en_US

Abstract:

Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.

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http://hdl.handle.net/10453/139851