Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

Landslides are regarded as one of the most common geological hazards in a wide range of geo-environment. The aim of this study is to assess landslide susceptibility by integrating convolutional neural network (CNN) with three conventional machine learning classifiers of support vector machine (SVM), random forest (RF) and logistic regression (LR) in the case of Yongxin Country, China. To this end, 16 predisposing factors were first selected for landslide modelling. Then, a total of 364 landslide historical locations were randomly divided into training (70%; 255) and verification (30%; 109) sets for modelling process and assessment. Next, the training set was used for building three hybrid methods of CNN-SVM, CNN-RF and CNN-LR. In the following, the trained models were used for landslide susceptibility mapping. Finally, several objective measures were employed to compare and validate the performance of these methods. The experimental results demonstrated that the performance of the machine learning classifiers previously mentioned can be effectively improved by integrating the CNN technique. Therefore, the proposed hybrid methods can be recommended for landslide spatial modelling in other prone areas with similar geo-environmental conditions.