*4.3. Displacement Prediction*

4.3.1. Trend Displacement Prediction

Trend displacement is driven by geological conditions. Therefore, the univariate AMLSTM NN model is used to predict the trend displacement. In order to verify the validity of the proposed model, the experiment will be benchmarked with LSTM, Random Forest(RF), RNN, and Support Vector Machine(SVM). The prediction results of the test dataset are shown in Figure 10.

It can be seen in Figure 10 that the trend displacement of the ZG118 and XD01 points represent a smooth monotonically properties. The prediction work by the SVM shows the worst, and the prediction values of the AMLSTM, LSTM, RNN, and RF models show high agreement with the measured true value. The relative error analysis in Table 1 indicates that the AMLSTM, LSTM, and RF have excellent performance in trend term prediction work.

**Figure 10.** The prediction results of trend displacement by different methods: (**a**) ZG118, (**b**) XD01.


**Table 1.** The accuracy assessment of trend displacement by different prediction models.

#### 4.3.2. Periodic Displacement Prediction

Periodic term is a key component for displacement prediction. According to the analysis in Section 4.1, the external periodic rainfall and reservoir water level both have an important influence. In this section, the periodic displacement will be predicted by the multivariate AMLSTM, and the multivariate LSTM, the SVM, the RF, and the RNN are used as benchmarks. The predictive periodic displacements by the five models are shown in Figure 11 and Table 2.

**Figure 11.** The prediction results of periodic displacement by different methods: (**a**) ZG118, (**b**) XD01.


**Table 2.** The accuracy assessment of periodic displacement by different prediction models.

As shown in Figure 11, the predictions of the AMLSTM and LSTM methods are clearly better than the others, and the quantitative analysis suggest that the AMLSTM achieved the best performance, along with RMSE, MSE, and R2, in periodic displacement prediction.

#### 4.3.3. Residual Displacement Prediction

Traditionally, the residual term can be regarded as the noise, which is removed during the decomposition procedure. Throughout the test, the residual term does not belong to the white noise. Therefore, the prediction work of this term is necessary. In this experiment, the univariate AMLSTM, LSTM, SVM, RF, and RNN models are used to predict the residual displacement prediction.

Compared with the trend and the periodic term, the residual term is harder to adopt in a model because of its random characteristic. As shown in Figure 12 and Table 3, the AMLSTM offers a better prediction effect than the other four models.

**Figure 12.** The prediction results of residual displacement by different methods: (**a**) ZG118, (**b**) XD01.


**Table 3.** The accuracy assessment of residual displacement by different prediction models.

4.3.4. Total Displacement Prediction

The predicted cumulative displacements can be obtained by taking the sum of the trend, period, and residual displacements. The results are shown in Figure 13 and Table 4.

**Figure 13.** The prediction results of cumulative displacement by different methods: (**a**) ZG118, (**b**) XD01.


**Table 4.** The accuracy assessment of cumulative displacement by different prediction models.

The results show that, although some of the prediction values slightly deviate from the real measured data, the AMLSTM model shows the best performance, because this model not only considers multiple external factors, but also optimizes the LSTM algorithm by adding an attention layer. It can better reflect the response relationship between displacement and trigger factors. Moreover, the cumulative displacements are predicted badly by the SVM and RF models.

From a quantitative point of view, the RMSE and MAE of the AMLSTM model are lower than the LSTM, RNN, SVM, and RF models. These results reveal that the AMLSTM shows the most stable prediction performance. Secondly, the R<sup>2</sup> of the AMLSTM are higher than the others. The results indicate that the AMLSTM model has done the best accuracy prediction work. Therefore, the superiority of the AMLSTM can be proved.

### **5. Conclusions**

The traditional landslide prediction model directly deletes the residual items. Moreover, most classic deep learning prediction models do not highlight the impact of important information on the results, so they cannot accurately predict the displacement. This paper used the CEEMDAN and the Attention Mechanism, combined with the LSTM NN to establish a dynamic prediction model for landslide displacement prediction. To corroborate its feasibility and applicability, the proposed model was applied to the Baishuihe landslide area, and joint multiple impact factors were considered here for prediction. By comparing to the prediction effects of other models, the prediction accuracy demonstrated a competitive performance. The results strongly suggest the effectiveness and feasibility of the AMLSTM model in landslide displacement prediction. This novel CEEMDANAM-LSTM strategy can be recommended to other landslide prediction works and has great potential in landslide risk assessment.

**Author Contributions:** Conceptualization, J.W. and G.N.; methodology, J.W.; software, J.W.; validation, S.W. and X.R.; formal analysis, S.G.; investigation, G.N. and S.G.; writing—original draft preparation, J.W.; writing—review and editing, J.W. and H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was financially supported by the National Key Research and Development Scheme Strategic International Cooperation in Science and Technology Innovation Program, grant number: 2018YFE0206500. National Program on Key Basic Research Project (973 Program), grant number: 2013CB733205.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The dataset we used in this paper includes the GNSS time series, rainfall and reservoir water level data set of Baishuihe landslide provided by Chinese National Cryosphere Desert Data Center (http://www.crensed.ac.cn/portal/, accessed on 9 February 2021). The authors acknowledge Google Earth for providing the map and Origin software. Thanks to the editor Aguero Gui and the anonymous reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.
