## 4 Conclusions  
Landslide deformation and damage occur under the joint action of various influencing factors such as geological conditions, rainfall, and groundwater. This study takes the Shangtan landslide as the research object and determines the interaction mechanism between the geological structure, mechanical mechanism, and landslide deformation by constructing a modifiable numerical model, and the damage mechanism of this landslide was analyzed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Time & G001 & G002 & G003 & G004 & G005 & G006 & G007 \\ \hline
7/15 & \(-\)75.2 & \(-\)32.5 & \(-\)531.0 & \(-\)838.3 & \(-\)34.1 & \(-\)25.0 & \(-\)23.0 \\ \hline
7/31 & \(-\)75.0 & \(-\)31.9 & \(-\)844.3 & \(-\)1188.3 & \(-\)34.2 & \(-\)23.4 & \(-\)22.6 \\ \hline
8/15 & \(-\)74.4 & \(-\)27.8 & \(-\)839.7 & \(-\)1187.7 & \(-\)36.4 & \(-\)21.6 & \(-\)21.2 \\ \hline
8/31 & \(-\)88.9 & \(-\)39.0 & \(-\)1128.7 & \(-\)1430.6 & \(-\)43.1 & \(-\)28.3 & \(-\)30.0 \\ \hline
9/15 & \(-\)112.0 & \(-\)49.6 & \(-\)1434.4 & \(-\)1689.8 & \(-\)46.6 & \(-\)29.9 & \(-\)37.7 \\ \hline \hline
9/30 & \(-\)109.5 & \(-\)44.7 & \(-\)1430.9 & \(-\)1684.7 & \(-\)44.5 & \(-\)30.6 & \(-\)29.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: July–September GNSS displacement monitoring sequence (unit/mm). Figure 12: Field inspection photos. (**a**) Around the station of GD01. (**b**) Station of GD03. (**c**) Station of GD03. (**d**) Around the station of GD04. by numerical simulation. Moreover, a novel approach using deep learning algorithms for displacement back-analysis was proposed for the first time. Based on the GNSS and rainfall data, we found the Shangtan landslide showed slow deformation, uniformly accelerated deformation, and variable accelerated deformation, with the largest cumulative displacement change in being observed in the north-south direction and the maximum deformation rate reaching 148.4 mm/d. Furthermore, the concentrated rainfall in the rainy season led to the softening of the sub-clay layer and a reduction in the slip resistance strength, which was the main reason for the variable accelerated deformation of the landslide. Particularly, to determine the shear strength parameters of the sub-clay layer, two DBA-ML models were constructed, and both algorithms showed satisfactory performance in terms of prediction accuracy, demonstrating relative errors of less than 5%. The DBA-LSTM algorithm improved the computational accuracy by 62.0% compared to DBA-BPNN, implying that the deep learning algorithm has better landslide displacement back-analysis performance. However, the prediction ability of both algorithms was dependent on the accuracy of the model parameter settings. Therefore, the application of the Auto-ML technique for displacement back-analysis to enable the model to learn the appropriate parameters automatically is also one of the important works in the subsequent research. Furthermore, to accurately assess the degree of landslide stability, a novel landslide warning method based on DBA-LSTM and the numerical analysis algorithms was proposed. Two sets of displacement warning criteria corresponding to two levels of the metastable state and unstable state were determined by numerical simulations. The validation results show that the criteria in this paper are more reasonable than those of the empirical warning model and have a certain reference value for landslide hazard warnings. Conceptualization, W.D. ; methodology, Y.D. and W.D. ; writing--original draft preparation, Y.D. ; writing--review and editing, W.D. and W.Y. ; resources, D.B. ; visualization, Y.D. ; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 42174053) and the Natural Science Foundation of Hunan Province, China (Grant No. 2021 JJ30805). Not applicable. The data presented in this study are available on request from the corresponding author.