Evaluation of Landslide Susceptibility in Chinese Loess Plateau Based on IV-RF and IV-CNN Coupling Models

Due to the complex interaction between geological environment and human activities, the Chinese Loess Plateau (CLP) is prone to frequent landslides. It is urgent to carry out landslide vulnerability assessment, selecting suitable influencing factors and training models. In this study, the CLP was taken as the study area. Based on field landslide survey and data collection, an evaluation system including topography, basic geological environment, meteorology and hydrology, human activities, soil physical and chemical properties, and vegetation coverage was built. The information model (IV) was used to connect the random forest model (RF) and convolutional neural network model (CNN) to build coupling models IV-RF and IV-CNN, and landslide susceptibility evaluation research was carried out. The results show that the accuracy of the coupling model (IV-RF, IV-CNN) is higher than that of the independent model (RF, CNN), and the AUC values of the four models are 0.916, 0.938, 0.878, and 0.853, respectively. The IV-CNN has stronger prediction ability and accuracy. The areas of extremely high, high, medium, low, and extremely low vulnerability areas in the IV-CNN model account for 8.78%, 7.47%, 15.34%, 19.82%, and 47.87% respectively, which are mainly distributed in the mountainous and loess hilly areas with complex geological environment and strong human activities in the south and east of the loess plateau. Slope, erosion type, landform type, clay content and distance from the road rank in the top five in the contribution rate analysis, and are the main control factors affecting the landslide development. The purpose of this study is to provide reliable scientific basis for the prediction and prevention of landslide disasters in the CLP, deepen the modeling idea for landslide vulnerability evaluation research, and optimize the uncertainty of independent model evaluation results.