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

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.

Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model

“…The more the distance from the road increases, the more the probability of a landslide decreases. This negative trend was observed by other authors worldwide [62][63][64][65], highlighting the importance of anthropogenic impact on slope stability. Additionally, other authors [26,66] reported that the landslide susceptibility gradually increases up to a proximity zone of 200 m from a road network.…”

Landslide risk management using geospatial technique: comparative insights of China and Indonesia

“…Moving forward, it is crucial to enhance and refine GIS-based techniques for landslide risk assessment (Nseka et al, 2021). Additionally, there is a need for continuous monitoring and updating of spatial data to ensure the accuracy and reliability of landslide susceptibility maps (Pasang and Kubíček, 2020). However, it is essential to integrate other factors, such as land use changes, climate change, and human activities, into landslide risk assessment models.…”

Remote Sensing and GIS-based landslide susceptibility mapping using bivariate statistical approach of Chhukha District

“…While often natural in origin, many landslides are triggered and aggravated by human-induced factors. Destruction of infrastructure, disruption of transportation and telecommunications networks, washing away of fertile soil, and fatalities are some serious risks associated with landslides in Bhutan (Pasang and Kubíček 2020). To mitigate the landslide consequences, disaster management is explicitly recognized as a part of sustainable development goals.…”