PASANG, Sangey and Petr KUBÍČEK. Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan. MDPI geosciences. Switzerland: Multidisciplinary Digital Publishing Institute, vol. 10, No 11, p. 1-26. ISSN 2076-3263. doi:10.3390/geosciences10110430. 2020.
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Original name Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan
Authors PASANG, Sangey and Petr KUBÍČEK.
Edition MDPI geosciences, Switzerland, Multidisciplinary Digital Publishing Institute, 2020, 2076-3263.
Other information
Original language English
Type of outcome Article in a journal
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
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Organization Přírodovědecká fakulta – Repository – Repository
Doi http://dx.doi.org/10.3390/geosciences10110430
UT WoS 000593891400001
Keywords in English landslide susceptibility mapping; road corridor; geographic information system; information value model; weight of evidence model; logistic regression model
Links MUNI/A/1251/2017, interní kód Repo.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 14/5/2021 01:58.
Abstract
In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.
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