Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan

Abstract

The Karakoram Highway (KKH) is an important route, which connects northern Pakistan with Western China. Presence of steep slopes, active faults and seismic zones, sheared rock mass, and torrential rainfall make the study area a unique geohazards laboratory. Since its construction, landslides constitute an appreciable threat, having blocked the KKH several times. Therefore, landslide susceptibility mapping was carried out in this study to support highway authorities in maintaining smooth and hazard-free travelling. Geological and geomorphological data were collected and processed using a geographic information system (GIS) environment. Different conditioning and triggering factors for landslide occurrences were considered for preparation of the susceptibility map. These factors include lithology, seismicity, rainfall intensity, faults, elevation, slope angle, aspect, curvature, land cover and hydrology. According to spatial and statistical analyses, active faults, seismicity and slope angle mainly control the spatial distribution of landslides. Each controlling parameter was assigned a numerical weight by utilizing the analytic hierarchy process (AHP) method. Additionally, the weighted overlay method (WOL) was employed to determine landslide susceptibility indices. As a result, the landslide susceptibility map was produced. In the map, the KKH was subdivided into four different susceptibility zones. Some sections of the highway fall into high to very high susceptibility zones. According to results, active faults, slope gradient, seismicity and lithology have a strong influence on landslide events. Credibility of the map was validated by landslide density analysis (LDA) and receiver oper-ator characteristics (ROC), yielding a predictive accuracy of 72 %, which is rated as satisfactory by previous researchers.Published by Copernicus Publications on behalf of the European Geosciences Union.

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

“…As the study region is small and we only focus on one area, the regional background condition, the voltage U, is defined as a fixed value of 1 V. The combined action of trigger factors and internal factors to prevent disaster occurrence is R. In this paper, R is assumed to be 1 and the resistance of the trigger and internal parameter systems are each thought to be 0.5. The importance of all related factors for the disaster events was determined based on previous studies of landslides and debris flows [46][47][48][49][50][51][52][53]. Where the importance was greater, the resistance value was smaller.…”

Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan

“…The Karakoram Highway (KKH) which runs parallel to sub-parallel to CPEC in North-Pakistan (in study area), was constructed in 1974-1978 and opened in 1979 usually blocks for few months every year for traffic due to the landslides. Dubair-Dudishal section of KKH (about 150 km) which is most affected by the landslides and almost half of the section is being destroyed by landslides every year (Ali et al 2019;Ali et al 2021). In this study, the potential slopes as well as the failed slopes along this section within a buffer zone of 8 km on both sides of the KKH were investigated through satellite images followed by field check (Figs.…”

The assessment of earthquake-triggered landslides susceptibility with considering coseismic ground deformation

“…This type of method is based on the assumption that future landslides would be easily to occur under similar conditions to those of the previous landslides. By analyzing the characteristics of the current landslides, a set of influencing factors are usually selected to implement statistical learning and evaluate the landslide susceptibility map (Pham et al, 2017;Ali et al, 2019;Lin et al, 2019). At present, many statistical learning methods have been used successfully to calculate the landslides susceptibility index (LSI) and generate the earthquake-triggered landslide susceptibility maps (Hong et al, 2017;Pham et al, 2016;Xu et al, 2012a;Yi et al, 2019).…”