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

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

Ali, S.;Ali, S.;Biermanns, P.;Haider, R.;Reicherter, K.;
natural hazards and earth system sciences 2019 Vol. 19 pp. 999-1022
358
ali2019landslidenatural

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 operator characteristics (ROC), yielding a predictive accuracy of 72 %, which is rated as satisfactory by previous researchers.

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