# Landslide Prediction with Model Switching +
## 5 Experiments
### Evaluation of Model Switched Landslide Prediction System  
In the following experiments, the same datasets as used in Section 5.1 were employed to evaluate our landslide prediction model. To evaluate the proposed MoSLaPS model, several performance indicators were used and defined as follows:  
* Mean Absolute Percent Error (MAPE) is defined as in Equation (26), where \(n\) is the number of predicted data, \(A_{t}\) is the actual value, and \(P_{t}\) is the predicted value. \[MAPE=\frac{1}{n}\sum_{t=1}^{n}\frac{|A_{t}-P_{t}|}{P_{t}}.\] (26)
* Root Mean Squared Error (RMSE) is defined as in Equation (27), where \(n\) is the number of predicted data samples, \(y_{i}\) is the actual FS value, and \(\hat{y_{i}}\) is the predicted FS value. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}.\] (27)
* Normalized Root Mean Squared Error (NRMSE) is defined as in Equation (28), where \(\hat{y}\) is the mean of the actual values. \[NRMSE=\frac{RMSE}{\hat{y}}.\] (28)  
Figure 13 shows the actual FS and predicted FS for every 10 min. A BPNN prediction model is not able to learn the pattern of unstable class, as the size of the unstable class is much smaller than that of the stable class. If the original past data were used, the BPNN was not able to predict landslide  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **ACC** & **TPR** & **FPR** \\ \hline Event-Class & 97.94\% & 98.40\% & 2.01\% \\ BPNN & 97.16\% & 57\% & 0.42\% \\ SVM & 90.42\% & 78.06\% & 9.02\% \\ Adaboost & 98.27\% & 75.1\% & 0.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of event-class predictor with other common classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **ACC** & **TPR** & **FPR** & **TP** & **FP** & **TN** & **FN** \\ \hline
1 & 96.43\% & 100.00\% & 3.60\% & 2 & 9 & 241 & 0 \\
2 & 97.62\% & 94.44\% & 2.14\% & 17 & 5 & 229 & 1 \\
3 & 98.41\% & 95.45\% & 1.30\%  
occurrences. However, after processing the training data using the ADASYN algorithm to re-balance the distribution of classes, a single BPNN prediction model was still not able to learn the pattern of unstable class perfectly. This is because the data of the stable and unstable classes affected each other. As a solution, two BPNN prediction models Were proposed, one to learn the pattern of the stable class and another to learn the pattern of the unstable class. To switch between two BPNN models, an event-class predictor was constructed that can deal with imbalanced data distribution to predict the future class as a decision. Therefore, our proposed MoSLaPS method could learn the patterns of both the stable and unstable classes.  
We compared our proposed MoSLaPS method with the above-mentioned methods, including a single BPNN and ADASYN+BPNN. A single BPNN was described, in detail, in Section 3.1; and ADASYN+BPNN use the ADASYN algorithm to re-balance the training data. After re-balancing, the processed training data were applied to train the BPNN.  
Figure 13: Comparison of the proposed method with other BPNN methods.  
The error metrics were evaluated for MoSLaPS, ADASYN+BPNN, and BPNN. Compared with BPNN and ADASYN + BPNN, our method resulted in much smaller MAPE and RMSE. This means that the prediction accuracy of our method was higher. The NRMSE of our method was also larger than that of the others, which means that our prediction results were closer to the real situation.  
Although MoSLaPS was more accurate than the other methods, it requires a little more computation time and resources. As shown in Table 7, the simulation results of the different methods shows that the speed of MoSLaPS was a little slower than that of BPNN and ADASYN+BPNN, and its CPU and memory usages were higher than the others. This is because MoSLaPS took extra computational time and resources to deal with the imbalanced data classification and switching between the different predictors. In our experiment, the time interval was 10 mins; so there was ample time to deal with the process.