# Landslide Displacement Prediction Method Based on GA-Elman Model
## 4 Discussion and Conclusions  
Based on the displacement monitoring data of Jianshanying landslide in the rainy season in 2020, the model is verified and analyzed by dividing the training set and test set and using the displacement data of the last 20 days. The prediction results show that GA-Elman model can successfully predict landslide displacement and deformation in the case of continuous 1 day, 10 days and 20 days. The preliminary judgment can be made through the model. Under the long-term internal conditions of the landslide, the rainfall factor affects the change of landslide displacement to a certain extent. Considering the six prediction conditions, the prediction effect with rainfall factor is significantly better than that without rainfall factor, so the occurrence of landslide displacement is still affected by rainfall factor. The RMSE, MAE, MAPE and R\({}^{2}\) of the test set predicted by GA-Elman for 20 consecutive days under rainfall conditions are 0.548 mm, 0.420 mm, 0.412% and 0.9977, respectively. It can be proved that the model is also suitable for medium and long-term displacement prediction of such landslides. In addition, by comparing the prediction effect of GA-Elman model and Elman neural network model, the results show that the error of GA-Elman model is relatively smaller than that of Elman neural network. The test relative error of GA-Elman model is more accurate than that of Elman neural network. It is proved again that GA-Elman model is superior to the single Elman model in the prediction of landslide displacement. GA-Elman model also has good prediction accuracy, which can achieve accurate prediction of landslide displacement and meet general engineering application requirements. Therefore, according to the displacement and deformation data of the landslide obtained by the GA-Elman prediction model, the deformation rate and deformation acceleration of the landslide can be further calculated. When the predicted value of the landslide deformation reaches the critical early warning index of the landslide, the landslide can be analyzed and judged in time. In this way, the early warning information can be issued in time and the corresponding emergency treatment measures can be taken to effectively reduce casualties and property losses. Overall, because the dynamic deformation process of landslide will be affected by internal and external factors, different models for different influencing factors can effectively improve the prediction accuracy of landslide deformation. Therefore, more other influencing factors (such as climate, groundwater activity, etc.) should be introduced into the model research in future work to further improve the accuracy and precision of landslide displacement prediction. Figure 9: Relative percentage error of prediction with GA-Elman and Elman models. **Author Contributions:** Conceptualization, C.W., Y.Z. and L.B. ; methodology, C.W., W.G. and Q.M. ; validation, C.W., W.G. and Q.M. ; formal analysis, C.W., Y.Z., L.B., W.G. and Meng, Q.; investigation, C.W., W.G. and Q.M. ; resources, C.W., W.G. and Q.M. ; data curation, C.W., W.G. and Q.M. ; writing--original draft preparation, C.W., W.G. and Q.M. ; writing--review and editing, C.W., W.G. and Q.M. ; supervision, Y.Z. and L.B. ; project administration, C.W. and Q.M. ; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key Research and Development Program of China (No. 2018 YFC1504805 and No. 2019 YFC1509601), and Geological Survey Projects of China Geological Survey (No. DD20211369 and No. DD20190639). Not applicable for studies not involving humans or animals. Not applicable for studies not involving humans. This work was supported by National Key Research and Development Program of China (No. 2018 YFC1504805 and No. 2019 YFC1509601), Geological Survey Projects of China Geological Survey (No. DD20211369 and No. DD20190639). The authors declare no conflict of interest.