Research on the Impact of Random Negative Training Samples on the Spatial Quantitative Model of Landslide Hazards

Abstract

In the present study, a spatial quantitative model of landslide hazards based on a deep belief network (DBN) is constructed. Firstly, environmental similarity-based sampling (ESBS) was used to determine the negative sampling area. Secondly, multiple data sets are constructed. Each data set contains seven landslide-conditioning factors; 70% of the data are used for training; and 30% are used for validation. The performance evaluation index of the spatial quantitative model of landslide hazards was established; that is, the AUC mean (AUCmean) was used to measure the stability of the model, and the AUC standard deviation (AUCSD) was used to measure the uncertainty of the model. Finally, the accuracy of the prediction results of the DBN model is analyzed. The results show that the area with negative sample reliability greater than 0.51 is the best negative sample sampling area, and the stability of the DBN model is maintained at a relatively good level in both the training step (AUCmean = 0.9597) and the validation step (AUCmean = 0.8897). The standard deviation of AUC is close to 0 (AUCSD = 0.0081 in the training step and AUCSD = 0.0085 in the validation step), indicating that the selected negative samples have a weak impact on the performance of the model. The susceptibility areas of very high obtained by the DBN model (landslide points in the susceptibility areas of very high accounted for 55.03%) are realistic. Therefore, the DBN model constructed in the present study is effective and can be used in the field of landslide hazard spatial prediction.