Přehled o publikaci
2020
Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan
PASANG, Sangey a Petr KUBÍČEKZákladní 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
Organizace
Přírodovědecká fakulta – Masarykova univerzita – Repozitář
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.