# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 4 Case Study: Shangmao Landslide
### Validation  
To evaluate the reliability of the proposed model, we processed the dataset by two neural networks, MLP and LeNet, and compared their results with our results.  
The hyperparameter search process was the same as in the previous example.  
The MLP model uses three hidden layers. We set the dropout as 0.5. Adam optimizer was used to optimize the network. The Relu function was chosen for the activation function, and categorical cross-entropy was used for the loss function. The batch size was 32, the initial learning rate was 0.01 and the initial iteration number was 1500.  
The LeNet model uses five hidden layers and the Adam optimizer to optimize the network. The loss function is categorical cross-entropy. The batch size is 32, the initial learning rate is 0.001 and the initial number of iterations is 1500.  
To speed up the model training, the learning rate dynamic decay strategy and EarlyStopping (i.e., stop training when the accuracy is not increasing within 100 iterations) were used to stop iterations before overfitting and terminate training.  
The evaluation of the training results in Table 5 shows that the ResNet algorithm outperformed the MLP and LeNet algorithms.  
The early warning results of MLP and LeNet algorithms are shown in Figure 18. The results show that the ResNet method performs better in early warning than MLP and LeNet methods.  
We also compared the K-means-ResNet model-based warning with the landslide warnings obtained by the conventional warning method from [17], and the thresholds are listed in Table 6.  
The warning results of the conventional warning method are shown in Figure 19. There are some false warnings from 16 August 2021 to 28 May 2022. After 28 May 2022, the landslide deformation rate increased, and intensive warnings were generated. However, when the landslide deformation slowed down, there were still many missing alarms and false alarms.  
The proposed early warning method based on the K-means-ResNet model can effectively reflect the deformation state of landslides and reduce false alarms. In addition, this method does not need to determine the threshold value or dynamically adjust the threshold value based on experience, which is necessary for the traditional th  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(S_{ia}(\text{mm})\) & 20 & \(S_{ka}(\text{mm})\) & 40 \\ \(v_{21}(\text{mm}/\text{d})\) & 10 & \(v_{22}(\text{mm}/\text{d})\) & 20 \\ \(v_{31}(\text{mm}/\text{d})\) & 10 & \(v_{32}(\text{mm}/\text{d})\) & 20 \\ \(v_{33}(\text{mm}/\text{d})\) & 30 & \(v_{34}(\text{mm}/\text{d})\) & 40 \\ \hline \hline \end{tabular}  
Notes: \(S\) denotes the displacement threshold and \(v\) denotes the velocity threshold.  
\end{table}
Table 6: Thresholds of the Shangmao landslide.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** \\ \hline MLP & 0.957 & 0.926 & 0.381 \\ LeNet & 0.947 & 0.940 & 0.805 \\ ResNet & 0.993 & 0.993 & 0.825 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results obtained by the three models for the Shangmao landslide.  
Figure 19: Threshold warning results of the Shangmao landslide.  
Figure 18: Results of warning (**a**) MLP methods and (**b**) LeNet methods.