## 4 Discussion  
Affected by the complex environment in the field, there will be missing data in the process of landslide monitoring, which will affect the accurate analysis of landslide displacement. In landslide disaster monitoring, landslide displacement deformation is relatively complex, and the displacement changes of each deformation area on the landslide are not the same. From the analysis of landslide local deformation characteristics, there is a certain correlation between the daily displacement deformation of different monitoring points. In addition, landslide displacement is a continuous cumulative process, and the landslide displacement occurring in the preceding period will have a certain influence on the landslide displacement occurring subsequently. Therefore, in order to better realize landslide data completion and prediction, a novel model based on MLATC method is proposed, and the RM and NM cases are designed, respectively, so as to verify the validity and reliability of the designed model. In addition, from the construction of the model, the initial time series matrix is converted into a third-order tensor structure. Under the assumption that the time series data satisfy approximate low-rankness, the time series completion and prediction problem is transformed into low-rank tensor completion and prediction. The tensor-based completion method fully considers the time series of landslide displacement data, which not only preserves the correlation between different displacements, but also better fits the landslide displacement deformation characteristics, making the displacement completion more accurate.  
To verify the effectiveness of the algorithm, a comparative analysis with the existing RTMF and HALRTC models was performed. The MAPE and RMSE of the MLATC model are 0.9066, 0.9196 and 0.7676, 0.9880 for the NM5% and RM5% data completion, respectively. Similarly, the MAPE and RMSE for NM5% and RM5% data prediction are 1.1079, 3.6676 and 1.1084, 3.6774, respectively. The analysis results show that under the conditions of 5%, 10%, 20% and 40% missing data, the data completion and prediction effects of the MLATC model are better than those of other models, which also confirms the significant data completion and prediction effects of the MLATC model. In this study, the completion and prediction model were constructed by considering the intrinsic correlation between landslide displacement data and using the low-rankness of the completion tensor. Data completion is an iterative calculation of the landslide displacement time series based on the entire original data. In order to verify the effectiveness of the data completion model, random and non-random data missing cases are designed, and time series prediction is based on the predicted experimental data as the missing value, which also belongs to a special case of missing data. By default, this part of the experimental data is not involved in iterative calculations. Therefore, the data prediction for time series is the same as the method used for data completion. The model is implemented in a rolling prediction method with cyclic and iterative computation, which leads to a loss in the amount of data in the prediction case. The MAPE and RMSE values of NM and RM also show that the prediction effect of the MLATC model is lower than that of data completion, but both can achieve satisfactory results, which are more in line with the actual needs of landslide monitoring.