Landslide susceptibility mapping of Chilas area along Karakorum highway, Gilgit Baltistan, Pakistan
نویسندگان
1 Department of Earth Science, Karakorum international university Gilgit Baltistan Pakistan
2 College of Marine Science and Engineering, Nanjing Normal University, Nanjing, China
3 College of Marine Science and Engineering, Nanjing Normal University, Nanjing, China
4 Nanjing Normal University
5 School of Geography, Nanjing normal university
6 Department of Earth Sciences, Quaid I Azam University Islamabad,Pakistan
doi
10.30499/ijg.2022.329722.1399چکیده
The use of a Geographic Information System (GIS) for assessing landslide susceptibility in the steeply rugged mountainous terrain of Chilas Basin, Pakistan, is covered in this research. Chilas is the part of Karakorum mountain ranges that lie north of Gilgit. Northern Pakistan is the region in which all the catastrophic events like earthquakes, mass wasting, and flash floods are routine marvels. Among them, catastrophic landslide events in this highly elevated and steeply mountainous region are a severe threat to human as well as economic property. To assess these catastrophic landslide events, a detailed landslide inventory map was constructed based on Google Earth images. Followed by field observation in which the selected spots of a landslide triggered locations were confirmed in the field. Four main controlling parameter groups were collaborated to generate landslide susceptibility maps: (1) Human-induced parameters like road distance, (2) Topographical parameters in terms of slope, and land cover, (3) Hydrological parameters, like rainfall, distance to stream, and temperature (4) Geological parameters in term of lithology and distance from major faults. These thematic layers were developed in a GIS environment to construct the landslide hazard map of the Chilas Basin. Among all the controlling parameters slope is regarded as the highest-ranked factor as followed by geology and landcover. Analytical Hierarchy Process (AHP) basis weighted overlay technique was used to assess the final susceptibility map followed by Area under Curve (AUC) model. Based on these analyses, four distinct susceptible regions were detected in the area, with severe mass wasting activities. The AUC model gives an 81% result, which is satisfactory.