## 1 Introduction  
Since the impoundment of the Three Gorges Reservoir area (TGRA) in June 2003, many new landslides have occurred and some existing landslides have been reactive. The Shanshucao landslide took place on 2 September 2014, interrupting the power supply for the town and damaged around \(20\times 10^{4}\) m\({}^{2}\) area of orange planting [1]. The Honyanzi landslide took place on 24 June 2015, initiating a reservoir tsunami that resulted in two deaths and significant damage to shipping facilities [2]. As an essential component for landslide early warning systems, displacement predictions become ever more important in the TGRA. In general, landslide displacement predictions involve the following steps: (i) the decomposition of total displacement, (ii) the selection of candidate factors, (iii) the establishment of predicting models, and (iv) the evaluation of prediction results. For the decomposition of total displacement (step i), the moving average method (MAM) [3], the empirical mode decomposition [4], the wavelet analysis [5; 6], and the variational mode decomposition [7] have been proposed to obtain sub-sequences of total displacement. The obtained sub-sequences are usually defined as trend terms and periodic terms. The trend term of the original total displacement represents the component mainly caused by the state factors and the periodic term represents the component mainly caused by the influence factors. For the selection of input factors (step ii), the mutual information, the Pearson cross-correlation coefficient [4], and the grey correlation analysis method (GCAM) [8; 9] have been used. In the TGRA, the landslide deformation is the consequence of the state and the influence factors, and these controlling factors are very diverse [10; 11; 12]. Thus, it is necessary to select factors before establishing models. Since [PERSON] [13] proposed the empirical formula for landslide prediction, many approaches to landslide displacement prediction have been developed [14], including those based on creep theory, statistical theory, time-series analysis, and artificial intelligence methods [15; 16]. For artificial intelligence methods that have been applied in landslide displacement, the Gaussian process model [17], the neural network model [18; 19; 20], the Support Vector Machine (SVM) model [9; 21; 22], the Extreme Learning Machine (ELM) model [8; 23; 24], and the LSTM model [4; 25] have become popular. Moreover, these approaches can also be divided into two categories which are univariate forecasting methods and multivariate forecasting methods. Univariate forecasting methods only consider previous displacement, while the multivariate forecasting methods consider triggering factor as well. The current studies mainly focus on choosing single prediction models from the numerous artificial intelligence methods. Since some models many be only suitable for specific landslide, a specific type of landslides, or a set of landslides with specific local geological conditions, periods, and variations, it is necessary to study how to accurately and comprehensively different models predict landslides by considering advantages from different models [15; 16]. Moreover, the linear combination is perhaps most well developed and mature [26]. Therefore, this study mainly focuses on an Ensemble model with the weight coefficients based on linear combination theory. In this study, the accumulated displacements of the selected monitoring points on the Shengjibao landslide were decomposed into trend term and periodic term using the MAM. The trend terms were predicted using the univariate forecasting methods based on the LSTM and SVR algorithm, respectively. The periodic terms were predicted by the multivariate forecasting methods based on the LSTM and SVR algorithm with analyzing the relationship between landslide displacement and the triggering factors separately. The predictive total displacements of each model were obtained by adding their predictive trend and periodic terms.