Abstract
The purpose of this study is to assess the landslide risk for Hunza–Nagar Valley (Northern Pakistan). In this study, different conditioning factors, e.g., topographical, geomorphological, climatic, and geological factors were considered. Two machine learning approaches, i.e., logistic regression and artificial neural network were used to develop landslide susceptibility maps. The accuracy test was carried out using the receiving operative characteristic (ROC) curve. Which showed that the success and prediction rates of LR model is 82.60 and 81.60%, while 77.90 and 75.40%, for the ANN model. Due to the physiographic condition of the area, the rainfall density was considered as the primary triggering factor and landslide index map was generated. Moreover, using the Aster data the land cover (LC) map was developed. The settlements were extracted from the LC map and used as the elements at risk and hence, the vulnerability index was developed. Finally, the landslide risk map (LRM) for the Hunza–Nagar valley was developed. The LRM indicated that 37.25 (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones. This landslide risk map can help decision-makers for potential land development and landslide countermeasures.
Article Highlights
Landslide risk assessment is carried out using two machine learning algorithms
Social and demographic factors were used for preparing landslide index and vulnerability maps
37.25% (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones
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Details
1 University of Science and Technology Beijing, School of Civil and Resource Engineering, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705)
2 Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan (GRID:grid.442860.c) (ISNI:0000 0000 8853 6248)
3 Lulea University of Technology, Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea, Sweden (GRID:grid.6926.b) (ISNI:0000 0001 1014 8699)
4 Monash University Malaysia, Department of Civil Engineering, School of Engineering, Bandar Sunway, Malaysia (GRID:grid.440425.3)
5 Dasu Hydropower Consultant, Dasu, Pakistan (GRID:grid.440425.3)