Landslide susceptibility mapping model based on a coupled model of SMOTE-Tomek and CNN and its application: A case study in the Zigui-Badong section of the Three Gorges Reservoir area

China is a nation severely impacted by landslide disasters, which poses a great threat to the lives and properties of people in the disaster-affected areas. Landslide susceptibility assessment, as an important tool for landslide risk prediction, is of great significance for disaster mitigation and prevention. However, traditional landslide susceptibility assessment faces the issue of imbalanced data between landslide and non-landslide samples, leading to the inherent undersampling of non-landslide data in the training set. This results in the loss of important information features related to landslide events, thereby affecting the reliability of landslide susceptibility assessment. In this study, using the Zigui-Badong section of the Three Gorges Reservoir Area as an example, 14 evaluation factors, such as elevation and slope were chosen as landslide susceptibility assessment factors, and the original training set and the validation set were divided. In this study, the synthetic minority oversampling technique - Tomek Links (SMOTE-Tomek) method was employed to process the original training dataset, construct the input training set. A convolutional neural networks (CNN) was then trained using this input data, resulting in the SMOTE-Tomek-CNN coupling model. In addition, by intersecting the SMOTE-Tomek method with undersampling methods (random undersampling, RUS), they were separately coupled with the CNN model and support vector machine model (SVM) to form three coupled models: SMOTE-Tomek-SVM, RUS-CNN, and RUS-SVM. These were compared with the SMOTE-CNN coupled model. The results indicate that, among the four coupling models, the SMOTE-CNN coupled model has higher specific class accuracy and area under the ROC curve, with values of 73.60% and 0.965, respectively. This indicates that this method's predictive ability is superior to that of traditional methods, making it a reliable resource for landslide prediction in the studied area.