Landslide Susceptibility Modeling Using Bagging-Based Positive-Unlabeled Learning
Bangyu Wu, Weirong Qiu, Junxiong Jia, Naihao Liu
Abstract
Landslide susceptibility mapping is a practical approach for identifying landslide-prone areas. In this letter, we apply a semisupervised learning method, positive unlabeled-bagging (PU-bagging) to generate a landslide susceptibility map over a study area from the Loess Plateau in North-Central China. PU-bagging deals with the lack of negative samples in a training set and uses only positive and unlabeled samples. We prove the effectiveness of our approach by comparing the PU-bagging decision tree (DT) generated landslide susceptibility map with the ground truth (known landslide locations), as well as by comparing its performance with three widely used models (logistic regression, support vector machine, and artificial neural network). The promising results and the fact that the method is general urge us to believe that the PU-bagging should be able to perform in other landslide-prone areas where only positive samples are provided.