How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China

Abstract

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment (LSA). The study area is the Feiyun catchment in Wenzhou City, Southeast China. Two types of landslides samples, combined with seven non-landslide sampling strategies, resulted in a total of 14 scenarios. The corresponding landslide susceptibility map (LSM) for each scenario was generated using the random forest model. The receiver operating characteristic (ROC) curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy. The results showed that higher accuracies were achieved when using the landslide core as positive samples, combined with non-landslide sampling from the very low zone or buffer zone. The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA, which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Keywords

• Landslide susceptibility
• Sampling strategy
• Machine learning
• Random forest
• China