# Landslide Prediction with Model Switching +
## 5 Experiments
### Evaluation of Event-Class Prediction  
ADASYN [1] was applied for data pre-processing in this program. It synthetically created new samples from the unstable class to balance the distribution of the data, if required. In the ADASYN algorithm, the desired level of balance, \(\beta\), could be adjusted to control the number of new synthetic samples, which were needed when \(0\leq\beta\leq 1\).  
Our event-class predictor used ADASYN [1] for imbalanced data processing. After data pre-processing, the processed dataset was used to train the event-class predictor (i.e., a BPNN model). To evaluate the event-class predictor, several performance indicators were applied and defined, as follows:  
* True Positive Rate (TPR) is defined as in Equation (23);
* False Positive Rate (FPR) is defined as in Equation (24); and
* Accuracy (ACC) is defined as in Equation (25).  
If the prediction class was unstable and the actual class was also unstable, then the result was said to be a True Positive (TP). If the prediction class was stable and the actual class Was also stable, then the result was said to be a True Negative (TN). If the prediction class was unstable and the actual class was stable, then the result was said to be a False Positive (FP). If the prediction class was stable and the actual class was unstable, then the result was said to be a False Negative (FN). Table 3 shows the classification of the above four different categories.  
\[TPR=\frac{TP}{TP+FN}\times 100\%, \tag{23}\]  
\[FPR=\frac{FP}{FP+TN}\times 100\%, \tag{24}\]  
\[ACC=\frac{TP+TN}{TP+FN+FP+TN}\times 100\%. \tag{25}\]  
Figure 11: Monitoring curves of Factor of Safety and soil moisture at Shen-Mu station.  
In our experiment, Figure 12 shows the evaluation results of the ADASYN algorithm for different \(\beta\) levels. When \(\beta\) was greater than 0.45, the TPR was more than 0.98. Therefore, the balance coefficient \(\beta=0.45\) was selected. Table 4 shows the number of new synthetic samples. Majority represents the size of the stable class and Minority represents the size of the unstable class.  
The evaluation results of the event-class predictor are shown in Table 5. There were 10 sets of testing samples. The average ACC was 97.94%, the average TPR was 98.40%, and the average FPR was 2.01%. Due to the high accuracy of event-class predictor, it can be used to choose a decision to switch between the models of different data patterns.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**All Dataset** & **Majority** & **Minority** & **ADASYN** \\ \hline
1300 & 1280 & 20 & 449 \\
1300 & 1199 & 101 & 389 \\
1300 & 1222 & 78 & 424 \\
1300 & 1181 & 119 & 374 \\
1300 & 1199 & 101 & 420 \\
1300 & 1291 & 9 & 464 \\
1300 & 1227 & 73 & 414 \\
1300 & 1224 & 76 & 408 \\
1300 & 1056 & 244 & 299 \\
1300 & 1140 & 160 & 341 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of synthetic new samples generated by the ADASYN algorithm (\(\beta=0.45\)).  
Figure 12: The evaluation result of the ADASYN algorithm for different values of the coefficient \(\beta\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Class**} \\ \cline{3-4}  & & **Unstable** & **Stable** \\ \hline \multirow{2}{*}{**Prediction Class**} & **Unstable** & True Positive & False Positive \\  & **Stable** & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
Table 6 shows comparisons of the event-class predictor with other common classifiers, such as BPNN, Support Vector Machine (SVM), and Adaboost. The ACC of all classifiers were greater than 90%. A good classifier needs to have high TPR and low FPR. Although the FPR of our classifier, compared with BPNN and Adaboost, was a little higher, the TPR of our proposed classifier was much higher than that of the others. This means that our classifier exhibited a higher ratio of correct classification.