# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide
### Validation  
To evaluate the reliability of the proposed model, we also processed the dataset by two neural networks, MLP and LeNet, and compared their results with our results.  
Figure 10: ResNet warning results of the Zhongma landslide.  
Figure 9: ResNet model structure.  
They also use the random search algorithm to determine the optimal hyperparameters. The iteration is 20 times and the result is validated by a fivefold cross-validation technique. The numbers of hidden layers are 1, 2, 3, 4 and 5. The initial learning rate parameters are 0.1, 0.01, 0.001 and 0.0001. The batch size selection parameters ranged from 16 to 128 with a step size of 2, and the optimization function selection parameters included SGD, Adagrad, RMSprop and Adam.  
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 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 2 shows that the ResNet algorithm outperformed the MLP and LeNet algorithms.  
The early warning results of MLP and LeNet algorithms are shown in Figure 11. The results show that the ResNet method performed better in early warning than MLP and LeNet methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** \\ \hline MLP & 0.954 & 0.781 & 0.412 \\ LeNet & 0.975 & 0.921 & 0.798 \\ ResNet & 0.981 & 0.971 & 0.871 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the results obtained by the three models for the Zhongma landslide.  
Figure 11: _Cont._  
We also compare the K-means-ResNet model-based warning with the warning method in [23], and the thresholds are listed in Table 3.  
The early warning results are shown in Figure 12. Between 14 and 29 May 2019 and 20 September 2020 and 14 March 2021, the landslide deformation velocity fluctuated in a small range, so the landslide state did not change. However, the early warning system generated some false warnings.  
Figure 11: Results of (**a**) MLP methods and (**b**) LeNet methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(S_{ia}(\text{mm})\) & 15 & \(S_{ka}(\text{mm})\) & 30 \\ \(v_{21}(\text{mm}/\text{d})\) & 10 & \(v_{22}(\text{mm}/\text{d})\) & 15 \\ \(v_{31}(\text{mm}/\text{d})\) & 10 & \(v_{32}(\text{mm}/\text{d})\) & 15 \\ \(v_{33}(\text{mm}/\text{d})\) & 20 & \(v_{34}(\text{mm}/\text{d})\) & 40 \\ \hline \hline \end{tabular}  
* Notes: \(S\) denotes the displacement threshold and \(v\) denotes the velocity threshold.  
\end{table}
Table 3: Thresholds of the Zhongma landslide.  
Figure 12: Threshold warning results of the Zhongma landslide.  
The early warning method based on the K-means-ResNet model proposed in this paper 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 threshold warning method.