# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 1 Introduction  
Landslides cause significant casualties and property damage each year around the world [1; 2; 3]. With the rapid development of the global economy and society, and the concomitant intensification of climate extremes and human engineering activities, landslide disasters increase in frequency day by day. Notably, many large hydropower plants and reservoirs have been built around the world to reduce air pollution and carbon emissions from coal power stations; this often leads to landslides around the reservoirs [4; 5]. Such landslides are extremely harmful, not only affecting the safety of reservoirs and hydropower stations, but also impacting the surrounding residents, ships in the rivers, and the surrounding roads [6; 7]. Therefore, large reservoir landslides are a pressing problem for geological disaster prevention and reduction. It is of great significance to make reasonable and accurate predictions of land displacement in large reservoir areas. The process whereby landslides develop in large reservoir areas is a result of the interaction of many factors, which exhibit complex and nonlinear characteristics [8]. These factors can be divided into two categories: internal and external [9; 10; 11]. The internal factors include changes in landslide stress and geometry, while the external factors include local precipitation, changes in reservoir water levels, and snow melting. Due to the complex processes of geological change and the conditions of landslide formation, it remains challenging to accurately predict landslide displacement; as such, much geologicalresearch has been devoted to this issue. Since the Japanese scholar [PERSON] proposed the empirical prediction formula for landslide displacement in the 1960s, many scholars have devoted their efforts to the field of landslide displacement prediction, achieving some significant results [12; 13; 14; 15]. [PERSON] et al. systematically summarized the different types of landslide displacement prediction methods, classified these methods, and discussed their respective differences and characteristics [16]. At present, landslide displacement prediction models can be roughly divided into the following four types:  
1. The empirical prediction model. This kind of method uses strict derivation methods, such as mathematics and physics, to analyze a large number of landslide monitoring data and experimental data; this is combined with the transformation of relevant formulas to predict the occurrence of landslide displacement. Focusing on the intrinsic causes of landslide displacement, various parameters of landslides are expressed numerically and according to relevant mathematical formulas. Representative models include the [PERSON] model (1965), the HOCK method (1977), and the Crosta and Agliardi model (2012). However, the scope of application of the model is greatly limited by the lack of understanding of the nature of landslides; moreover, the prediction accuracy of the model is not high. 2. The statistical prediction model. This method uses the theoretical knowledge derived from modern mathematics to design a landslide prediction model. In contrast to the empirical prediction period, which focuses on the mathematical expression of the landslide's own mechanisms [17], this method includes an investigation and statistical analysis of the geological environment surrounding the landslide, as well as the external factors. At the same time, the prediction accuracy and application scope of the model are also significantly improved at this stage. The rapid development of the statistical prediction model is attributed to the emergence and widespread application of modern mathematical theories, such as mathematical statistics, gray system theory, and probability theory. In recent years, many new theories and methods have been formed. For example, [PERSON] et al. (2011) introduced the GM (1,1) model of gray system theory into the field of landslide displacement [18].