J 2020

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

PASANG, Sangey a Petr KUBÍČEK

Základní údaje

Originální název

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Autoři

PASANG, Sangey a Petr KUBÍČEK

Vydání

MDPI geosciences, Switzerland, Multidisciplinary Digital Publishing Institute, 2020, 2076-3263

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Organizace

Přírodovědecká fakulta – Masarykova univerzita – Repozitář

DOI

http://dx.doi.org/10.3390/geosciences10110430

UT WoS

000593891400001

EID Scopus

2-s2.0-85094620030

Klíčová slova anglicky

landslide susceptibility mapping; road corridor; geographic information system; information value model; weight of evidence model; logistic regression model

Návaznosti

MUNI/A/1251/2017, interní kód Repo.
Změněno: 14. 5. 2021 01:58, RNDr. Daniel Jakubík

Anotace

V originále

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.
Zobrazeno: 3. 7. 2025 13:10