Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling

Abstract

Many studies consider landslide susceptibility prediction as a binary classification problem when using machine learning methods, which requires both landslide and nonlandslide samples for modeling. Nevertheless, there are only landslide and unlabeled areas in the real world, and directly considering unlabeled areas as nonlandslide areas may cause bias and incorrect label assignment. In this article, 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 nonlandslide 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.

Keywords

• Adaptive sampling
• landslide susceptibility prediction (LSP)
• positive unlabeled (PU) learning
• sensitivity analysis
• uncertainty analysis