# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 1 Introduction  
Accurately predicting the displacement of a landslide under critical sliding state and during the sliding process can mitigate its negative influence on both human lives and economic loss. It is also a major issue to be solved firstly to control economically and scientifically the disaster of residual soil slopes. However, it is still a difficult and important task. Many scholars have carried out a lot of research on the prediction of landslide disasters and obtained many achievements [1; 2; 3; 4; 5; 6; 7; 8]. The load-unload response ratio method was used to predict the failure time of landslides [1; 2]. The temporal and spatial prediction of a shallow landslide induced by rainfall was carried out by using a combination of probabilistic and deterministic methods [3]. The occurrence times and displacement trends of landslides were predicted by using the cusp catastrophe theory, the polynomial model, the displacement critical-rate method and inverse velocity analysis [4; 5; 6; 7; 8; 9]. Observed changes in rainfall amounts were used to predict changes in landslide displacement rates from [10], which made future landslide movements relate to expected rainfall scenarios [11]. Modern numerical methods are able to simulate large mass movements, and there is an opportunity to utilize such methods to evaluate the risks of catastrophic damage if a landslide occurs [12; 13]. The coupling effect of periodic fluctuation of water level and rainfall, the displacement and monitoring-time curve of a landslide appears \"step-like\" [14; 15; 16; 17; 18]. Therefore, the cumulative displacement of a landslide should be decomposed into multiple displacement components with different scale characteristics of time series. Then, a polynomial regression model [19] and machine learning (backpropagation neural network [19], support-vectorregression machine [20], long short-term memory network [21; 22] and kernel extreme learning machine [23]) are used to train and predict each displacement component, respectively. Finally, by superimposing the prediction results of each displacement component, the cumulative displacement prediction of the landslide is obtained. Coupled with the time series of landslide displacement, these methods can make clear the physical meaning of each part of the displacement and reflect the relationship between each displacement component and the triggering factors to improve the accuracy of displacement prediction [18]. With the improvement of computer calculating ability and the development of machine learning and artificial intelligence, the disaster-prediction ability of landslides has been greatly improved. At present, the commonly used models for landslide displacement prediction include the backpropagation neural network (BPNN) [14; 15; 23], support-vector regression (SVR) [18; 24; 25; 26; 27; 28], extreme learning machine (ELM) [29; 30; 31; 32], kernel extreme learning machine (KELM) [14; 23; 33], long short-term memory (LSTM) [25; 34; 35; 36; 37], decision tree [38], and so on. Moreover, many algorithms are used to optimize the parameters of the prediction models, including the genetic algorithm (GA) [28; 39; 40], particle swarm optimization (PSO) [16; 26; 28; 29; 41; 42], fruit fly optimization algorithm (FOA) [18], grey wolf optimizer (GWO) [15], and so on. However, each algorithm has its limitations. Therefore, it is vital to select the appropriate machine-learning models for the accuracy of displacement prediction of landslides. The disaster of granite residual landslides involves infiltration characteristics, the intensity and pattern of rainfall, the transport mode of gas and liquid in soil, the temperature of the earth surface, the development mode of soil creep and their coupling interaction. This type of landslide is often induced by heavy rainfall and experiences multiple processes of sliding, relative stability, re-sliding, then relative stability and re-sliding, which pose a great threat to buildings and structures at the foot of the slope.