# Landslide Displacement Prediction Method Based on GA-Elman Model
###### Abstract  
The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation. landside displacement; prediction model; genetic algorithm; Elman neural network +
Footnote †: journal: Medical  
area. Therefore, it is particularly important to carry out deformation monitoring on the relative displacement and absolute displacement changes of landslide. Timely prediction of landslide displacement and deformation has important reference value for judging landslide deformation trend. Researchers have carried out a number of studies on landslide displacement prediction and have made some progress. For example, the empirical model is used to simulate the landslide displacement monitoring data. The common empirical models include Saito model, Hayashi model and Voight model. The above model uses the theoretical model of landslide creep to predict the displacement and deformation of landslide through a large number of experimental data. However, when the landslide is affected by other external factors, the model parameters need to be adjusted in time, otherwise the accuracy of the model will be reduced and the prediction effect will be affected. In addition, researchers also designed a series of landslide displacement prediction models through mathematical statistics, including grey system model [7], plant growth model and grey displacement vector angle model [8]. For example, [PERSON] et al. (2017) used grey model to construct different types of landslide displacement prediction methods [3]. With the continuous updating of artificial intelligence algorithms, some nonlinear models have been applied to landslide displacement prediction. The traditional nonlinear models mainly include catastrophe theory [9], cooperative work theory [10], back propagation neural network model [11; 12], support vector regression model [4; 13; 14], chaos model [15; 16; 17], decision tree model [18; 19], extreme learning machine model [20; 21], Elman neural network model [11; 14; 22; 23; 24] and so on. In the above models, Elman neural network has been widely applied to solve nonlinear system problems. The biggest difference between Elman neural network and traditional Back Propagation (BP) is that it has local memory function. Elman neural network is suitable for the prediction of time series because of its parallel processing, self-adaptation, self-learning and strong robustness. At present, some research scholars have carried out relevant research on the predictive evaluation of Elman neural network, such as [PERSON] et al.