## 3 Results and Discussion
### Establishment of DBA-LSTM Model
#### 3.2.2 Comparison of DBA-LSTM and DBA-BPNN  
The 49 sets of sample data in Table 3 were used as the training sets, the displacement data were treated as the input layer, and the target parameters to be inverted were treated as the output layer for training. According to the literature [19] and the method in this paper, two models, DBA-BPNN and DBA-LSTM, were constructed. By learning the training samples, we determined that the network structure of the DBA-BPNN model was 14-1 (100)-6, which denotes the number of input layers, hidden layers (number of neurons), and output layers, respectively, and the Levenberg-Marquardt algorithm was used as the training algorithm. It should be noted that the number of neurons in the hidden layer calculated by the empirical formula in the literature [19] does not achieve high prediction accuracy, and it was found that: the accuracy is optimal when the number of neurons in the hidden layer is 100; there are two hidden layers in the DBA-LSTM model; the number of neurons in each layer was set to 128 and 64; the tanh function was selected as the activation function; and the number of iterations was set as 10,000 times. The accuracy of DBA-BPNN and DBA-LSTM, respectively, was evaluated using the data in Table 4 as the test sets; Figure 8 shows the fit relationship between the predicted and theoretical values of the two algorithms and the goodness of fit. The six subplots on the left are the results between the predicted values of the DBA-BPNN algorithm and the theoretical values of the samples, corresponding to Figure 9a; the six subplots on the right are the results of the DBA-LSTM algorithm, corresponding to Figure 9b. Comparing the results of DBA-BPNN and DBA-LSTM, it can be found that both algorithms are in good agreement with the predicted and theoretical values, which indicates that the DBA-ML algorithm can effectively invert the equivalent physical and mechanical parameters of the landslide. However, from Figure 8, it is clear that the fitting effect of DBA-LSTM is significantly better than DBA-BPNN, and by counting the MAPE (average absolute percentage error) of the two algorithms, the accuracy of the DBA-LSTM algorithm (0.62%) is improved by 62.0% compared to the DBA-BPNN algorithm (1.63%). Among DBA-ML algorithms, the BPNN model was the earliest-applied and is the most frequently employed network model, and the experimental results in this paper show that the accuracy of DBA-LSTM is substantially improved compared to that of DBA-BPNN, but it does not deny the feasibility of DBA-BPNN. In order to further verify the accuracy of the two algorithms in this test, the inversion results of the two algorithms were entered into the numerical model to carry out forward calculation of the landslide displacement, and the actual input displacement data were compared to the actual input displacement data to calculate the differences in the absolute error. The results are shown in Figure 9, where the vertical coordinates correspond to seven groups of test samples. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Num. & & Output Layer & & & & Input Layer & \\ \hline
1 & 10 & 10 & 10 & 10 & 10 & 10 & \(-95.4\) & \(-57.8\) \\ \hline
2 &  
From the calculation results, it can be seen that for the DBA-BPNN algorithm, the maximum errors of the calculated displacements for this scheme in the Y-direction and Z-direction are 4 mm and 2 mm, respectively, but from Figure (a)a,c, it can be seen that DBA-BPNN only demonstrates large deviations at GD03 and GD04. Considering that the relative displacement of these two stations is much larger than others, by calculating the relative errors, it was found that the relative error is less than 5% for all of the stations in the seven groups of samples. Therefore, the DBA-BPNN algorithm is feasible for landslide inversion. The computational accuracy of the DBA-LSTM algorithm in both the Y- and Z-directions is less than 1 mm, which indicates that the landslide parameters in the inversion of the DBA-LSTM algorithm are more accurate.