Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)
The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.
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