# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 1 Introduction
In addition, there are gray vector angle models, models based on landslide slope changes [19; 20], etc. Most of these models are linear models, which show better results in predicting the displacement of landslides affected by a single factor, but have poor predictive effects for landslides with complex causes and many influencing factors. 3. The nonlinear prediction model. With the development and widespread application of system science and nonlinear science, scholars have realized that landslides are an open and complex system. To predict a landslide, qualitative discrimination and quantitative prediction must be combined to study the basic problems that lead to a landslide. Qualitative discrimination refers to the combination of precursor features, such as those exhibited prior to the evolution of the landslide, and the surrounding geological environment [21] Quantitative prediction refers to the quantitative analysis of the observed landslide displacement information data. During this period, BP and Elman neural network models were widely used [22; 23; 24; 25; 26]. The extreme learning machine model and the decision tree model have also been gradually introduced and applied to the field of landslide prediction [5; 27; 28]. 4. The comprehensive prediction model. When using a single nonlinear model to predict landslides, the application range and prediction accuracy of the model are sometimes limited [9]. In recent years, the comprehensive use of multiple models has become a new trend in the development of landslide prediction models. For instance, [PERSON] et al. proposed a landslide displacement prediction model based on GA-SVR [29], while [PERSON] et al. studied the WCA-ELM model, which is applicable to step-type landslides [30]. Methods for the decomposition of displacement data include empirical mode decomposition [31; 32], ensemble empirical mode decomposition [33; 34; 35], and variational mode decomposition [36; 37]. Although these methods can completely decompose the data and effectively improve prediction accuracy, the physical meaning of each component cannot be clarified due to the large number of components acquired (generally more than five); as such, they cannot effectively reflect the relationship between each displacement component and the influencing factors [38]. The Elman neural network has good dynamic characteristics and global stability, and it has been widely used to analyze and process nonlinear and dynamic complex data. [PERSON] et al. (2017) verified the feasibility of using the Elman neural network model in landslide monitoring and prediction [39]. Taking into account the nonlinear characteristics of landslide displacement monitoring data, they proposed an improved recurrent neural network based on Elman, and they proved the accuracy of the Elman neural network in short-term predictions. In addition, the research shows that a genetic algorithm (GA) can effectively improve the training speed and accuracy of the neural network by optimizing Elman's connection weight and threshold. In this study, the Shuizhuyuan landslide in the Three Gorges Reservoir area was taken as a case study. First, VMD was used to decompose the landslide displacement sequence into three subsequences: trend displacement, periodic displacement, and random displacement. Combined with the gray relational analysis method, the influencing factors that affect landslide displacement deformation were selected. Then, the training set, the test set, and the validation set were divided in the dataset. The GA-Elman model was used to predict and analyze the training set and the test set to determine the optimal training combination model. Finally, the displacement prediction of the landslide's cumulative displacement validation set was realized on the basis of the optimal prediction model.