Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

Abstract

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

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

“…Maximum landslides occurred within the slope range of 30-40°, it is mostly due to local variations in conditions (Ayalew and Yamagishi, 2005), which may include excessive rainfall, human activities including downslope cutting due to highway construction etc. Results show that the maximum landslides in Hunza-Nagar valley, northern Pakistan were found within the slopes having amount of dip ranging from 30°-50° (Bacha et al 2018;Riaz et al, 2018). In terms of aspect, the slope face of maximum landslides was towards south which is probably because of receiving maximum sunshine in daytimes followed by temperature decrease at night times, and this phenomenon helps in weathering processes which provide base for maximum potential slopes.…”

“…However, the case was slightly different for faults distance. Nevertheless, landslides were concentrated around the faults and were rapidly decreasing with distance (Bacha et al, 2018); however, near Sazin and Dudishal area the landslide activity was found very high even there is no major fault in that area, and this is maybe due to either glacier activity (as there large old landslides deposits that maybe the moraine deposits) or due change in lithology in that area (as maximum landslides were found in Chilas-complex lithology which is present in this area); lithology is one of the most important parameters to determine the occurrence of landsliding in northwest Himalayas in Pakistan (Riaz et al, 2018). In addition to this, Mansehra granite seems to be most stable while the maximum landslides were found in Chilas-complex (61.7 %) followed by Kamila amphibolites (22.9 %).…”

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

“…Many authors used an AHP-based model to prepare susceptibility maps (Ahmed, 2015;Arizapa et al, 2015;Bachri and Shresta, 2010;Basharat et al, 2016;Intarawichian and Dasananda, 2010;Kamp et al, 2008;Komac, 2006;Park et al, 2013;Pourghasemi et al, 2016Pourghasemi et al, , 2012Pourghasemi and Rossi, 2017;Rahim et al, 2018;Rozos et al, 2011;Shahabi and Hashim, 2015;Yalcin, 2008). Comparison of AHP-based models with other models in some studies (Pourghasemi et al, 2012(Pourghasemi et al, , 2016Pourghasemi and Rossi, 2017;Shahabi and Hashim, 2015;Yalcin, 2008) proved the former to be more accurate and precise.…”

“…Weighted overlay method (WOM) is a simple and direct tool of Arc GIS to produce a susceptibility maps (Bachri and Shresta, 2010;Intarawichian and Dasananda, 2010). Many researchers used WOM to produce landslide susceptibility map (Bachri and Shresta, 2010;Basharat et al, 2016;Intarawichian and Dasananda, 2010;Roslee et al, 2017;Shit et al, 2016). We used an overlay of raster layers of all controlling factors to prepare a susceptibility map.…”

Regional Landslide Identification Based on Susceptibility Analysis and Change Detection

“…Merghadi [17] performed a landslide susceptibility assessment using several machine learning methods to generate a landslide prone area. Meanwhile, several studies have also used the machine learning approach to calculate susceptibility and all received good results [18][19][20]. Other research has used DEM (Digital Elevation Model) or terrain models to analyze surface geomorphological features and landslide-induced changes because they provide more detailed geomorphological features [21][22][23].…”