Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping

The selection of nonlandslide samples is a key issue in landslide susceptibility modeling (LSM). In view of the potential subjectivity and randomness in random sampling, this paper considers LSM as a positive-unlabeled (PU) learning problem and proposes a two-step deep neural network framework (T-DNN). Through the Spy technique and iteratively training binary classifiers, negative samples with high confidence were identified from the random subsamples with unlabeled sets. Based on the framework and traditional random sampling, we used logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) models for testing and validation. Taking the Changbai Mountain Area in Jilin Province, China, as an example, according to the regional landslide list and the metrological, geographical, and human factors of frequent disasters, landslide susceptibility was evaluated. Results show that the proposed T-DNN method can enhance the selection of negative samples and make the results of landslide susceptibility assessment more reliable and accurate; the area under the receiver operating characteristic curve (AUC) reaches 0.953. In addition, compared with traditional random negative sample sampling, the optimized sample set shows more stable and superior prediction performance in different classifiers.