Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China

A hybrid framework by integrating stacking ensemble with two deep learning methods of convolutional neural network (CNN) and recurrent neural network (RNN) is introduced in this paper for landslide spatial prediction in the Three Gorges Reservoir area, China. The proposed framework is summarized in following steps. First, a spatial database consists of 20 landslide conditioning factors and 196 landslide polygons was established. Then, landslide and non-landslide pixels were randomly divided into training (70% of the total) and test (30%) sets. Next, a stacking ensemble method that integrates CNN and RNN was constructed using the training set. Finally, the proposed stacking framework was applied for landslide susceptibility mapping and evaluated. Experimental results demonstrated that the proposed framework can obtain the best predictive capability (0.918) than CNN (0.904), RNN (0.900) and logistic regression (0.877) in terms of area under the receiver operating characteristic curve (AUC). Therefore, it can be useful for landslide disaster management and assessment.

Automated summary

This paper introduces a hybrid framework integrating stacking ensemble with convolutional neural network (CNN) and recurrent neural network (RNN) for landslide spatial prediction in the Three Gorges Reservoir area, China. Experimental results demonstrate that the proposed framework achieves the best predictive capability in terms of AUC compared to CNN, RNN, and logistic regression, which is significant for landslide disaster management and assessment.