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.
Highlights
A landslide is defined as “the movement of a mass of rock, earth, or debris down a slope”, and they are classified according to the type of slope movement, type of material involved, and the speed of movement [1,2]
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
landslide susceptibility map (LSM) were generated from the information value (IV), weight of evidence (WOE), and Logistic regression (LR) models considering the relationship between causal factors and landslides
Summary
A landslide is defined as “the movement of a mass of rock, earth, or debris down a slope”, and they are classified according to the type of slope movement (fall, topple, spread, flow, slide), type of material involved (rock, earth, debris), and the speed of movement [1,2]. Are some of the risks associated with a landslide event that can translate into major social impacts and economic loss. Expansion of human settlement into geologically sensitive areas, infrastructure development, and increased agricultural practices result in land use changes that further aggravate the problem of landslides and associated risks [5,6,7]. Cutting slopes for infrastructure development, during road construction, is a major triggering factor for most landslides. Landslides impede socio-economic activities, such as the development of efficient transportation networks, reservoirs, settlement areas, and agricultural fields, especially in mountainous regions [8]. To support sound decision-making in building up a national socio-economic development plan that addresses the impact of landslides, information based on risk analysis and landslide assessment concerning the likelihood of landslide occurrences are useful
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