Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling
Zhice Fang, Yi Wang, Ruiqing Niu, Ling Peng
Abstract
Many studies consider landslide susceptibility prediction as a binary classification problem when using machine learning methods, which requires both landslide and non-landslide samples for modelling. Nevertheless, there are only landslide and unlabeled areas in the real world, and directly considering unlabeled areas as non-landslide areas may cause bias and incorrect label assignment. In this study, we present a positive unlabeled learning method coupled with adaptive sampling and random forest (AdaPU-RF) to predict landslide susceptibility in the Three Gorges Reservoir area, China. This method can make full use of the landslide and non-landslide information contained in unlabeled areas. Experimental results show that the AdaPU-RF method achieves desirable predication outcomes in terms of accuracy analysis, sensitivity analysis and uncertainty analysis. Overall, the application of AdaPU-RF provides a new perspective for landslide susceptibility prediction, and can be recommended for other areas with similar geo-environmental conditions.