# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Prediction of Trend Displacement  
When the trend component data of the displacement were substituted into the model for prediction, the model prediction results under different training sets were compared using evaluation indices such as MAPE (mean absolute percentage error), MSE (mean square error), RMSE (root-mean-square error), and R\({}^{2}\) (coefficient of determination). When the GA-Elman model is used for training and prediction, it is necessary to determine the values of the relevant parameters involved in the model. Through multiple trial calculations, the relevant parameters of GA were set as follows: the number of population iterations was 30, the population size was 30, the crossover probability was 0.3, and the mutation probability was 0.1. In the setting of relevant parameters for the Elman algorithm, the learning rate was 0.5, the momentum factor was 0.9, and the model accuracy was 0.0001.  
The operation results, generated after the trend component dataset was substituted into the model, are shown in Figure 8a-d. It can be seen that, when the training set was 40 weeks, 60 weeks, and 80 weeks, there was a concave phenomenon of nearly 5 mm in one section of the trend displacement. By verifying the original monitoring data of the monitoring points, it was confirmed that there was a decrease in one section of the displacement monitoring data. Through further observation and analysis, no obvious changes in the precipitation data and the water level data for the reservoir area were identified during this period. On the basis of the situation detailed above, it can be suggested that the concave displacement of the trend term in this section was due to the influence of external environmental factors (search satellite, fog, moisture, etc.), which influenced the monitoring accuracy. However, this did not affect the overall working performance of the monitoring equipment. The results of the runs show that the prediction accuracy of the model tended to increase and then decrease with the increase in the base data in the training set. The relevant evaluation index values in the prediction results are shown in Table 3. According to the operation results in Table 3, the MAPE, MSE, RMSE, and R\({}^{2}\) of Model 2 were 0.06%, 0.0361, 0.9876, and 0.0217, respectively, which are better results than those produced by the other models. The prediction accuracy of this model was the highest. That is to say, in the prediction process of trend displacement, when the basic training set data volume was 40 weeks, the model prediction effect was the best. This indicates that the timeliness of the trend displacement component data of the landslide was strong, and the real-time monitoring of landslide displacement data must be strengthened.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model Number**} & \multirow{2}{*}{**Component Type**} & \multicolumn{4}{c}{**Influencing Factors**} \\ \cline{3-6}  & & **P1** & **P2** & **P3** & **P4** \\ \hline \multirow{3}{*}{Model 1} & Periodic component & 0.5769 & 0.5751 & 0.5747 & 0.5734 \\  & Random component & 0.5663 & 0.5693 & 0.5690 & 0.5685 \\  & Periodic component & 0.8505 & 0.8509 & 0.8507 & 0.5349 \\  & Random component & 0.9759 & 0.5920 & 0.9762 & 0.9764 \\  & Periodic component & 0.6532 & 0.6559 & 0.6544 & 0.6703 \\  & Random component & 0.8916 & 0.6057 & 0.9194 & 0.9139 \\  & Periodic component & 0.6293 & 0.6300 & 0.6297 & 0.6357 \\  & Random component & 0.8234 & 0.8957 & 0.9099 & 0.6676 \\ \hline \end{tabular}
\end{table}
Table 2: Analysis of the relational degree between the displacement component and the influence factor component.