Abstract
Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH.
Highlights
Landslides are the results of changes in environmental parameters
A major component of the environment of the highway is the occurrence of mountain glaciers, which result in major glacial debris flows and rock avalanches [3]
The Area under Curve (AUC) value of the evaluation results and the correct classification ratio of the verification points demonstrate that the Random Forest evaluation results are superior to those of the logistic regression
Summary
Landslides are the results of changes in environmental parameters. Under the influence of extreme climatic events, an increasing number of landslides are occurring worldwide, which results in major economic and human losses [1]. This research makes up for the lack of historical disaster data and the evaluation gap on the regional scale, using SBAS-InSAR results to optimize landslide susceptibility degrees, which can greatly improve the accuracy of landslide susceptibility assessment at a regional scale. After the emergence of GIS (Geographic Information System) technology, numerous quantitative approaches to landslide susceptibility were developed, such as Logistic Regression, Analytic Hierarchy Process, Artificial Neural Networks, Support Vector Machines, and Random Forest All of these methods have been shown to have specific advantages in different study areas and for different sets of factors involved in disasters [9,10,11,12,13]. SBAS-InSAR deformation results to correct the errors, which allows the optimized susceptibility map to provide more reliable decisions for land use and landslide prevention and mitigation
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