# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 3 Results and Discussion
### Influence of Signal Attributes  
Figure 8 shows the distributions of individual attribute values for the three classes of events calculated by all 642 training records. No single attribute could perfectly discriminate the three classes of events, but to some extent, the differences among the distributions of individual attribute values were associated with different classes. The difference in attribute values between earthquake and noise events was rather large in comparison with the attribute values of landslide events, which are prone to have values in between. Thus, a classifier integrating many attributes is necessary for desirable classification results. Two kinds of classifier were built to evaluate the importance of the attributes. The first was single-attribute classifiers, and the second was 23-attribute classifiers, which were composed of all possible combinations of 23 of the 24 attributes. The relative importance of the attributes in the classification is presented in Figure 9. The respective number of attributes refers to Table 1, the first nine of the respective numbers are time domain attributes, and the rest belong to the frequency domain. In Figure 9a, the black line represents the result of the proposed classifier using all attributes, and the accuracy was 91.3%; the blue line indicates the result of the 23-attribute classifier, which omitted the numbered attribute and relied on the rest of the 23 attributes; and the red line indicates that for the single-attribute classifier. In other words, the blue line demonstrates the variation of the accuracy if a single attribute is lost, and the red line illustrates the accuracy of a single attribute. Through testing of single-attribute classifiers, we observed that the accuracy of a single attribute was largely distinct among the others, and the most discriminant attribute was the moving average ratio (MAR, T3), with accuracy of 83.7%. The least discriminant attribute was the PSD between 0.02 and 0.05 Hz (LF1, F10), with accuracy of 53.8%. The mean accuracy of the classifiers created by time-domain attributes was higher than that created by frequency-domain attributes. Although some of the single-attribute classifiers had lower accuracy, omitting any one of the attributes decreased the accuracy by at least 2%-3%, which implies that every attribute had a certain amount of influence on the classification result. An attribute contributes improvement to a classifier if its accuracy is beyond 50%. To clarify the role of an attribute in a classifier, the single-attribute classifiers with the lowest accuracy in the time domain and frequency domain are discussed as follows. 1. T2 Attribute (\(\sigma\)MA classifier)  
The accuracy of the \(\sigma\)MA single-attribute classifier was 65.6%, as shown as Table 4, but its sensitivity or precision for noise events was higher than 90%. In the confusion matrix, for identifying landslides, the sensitivity value is obtained by dividing the correctly identified landslide signals by the manually identified landslide signals, whereas the precision value is obtained by dividing the correctly identified landslide signals by all of the classified landslide signals. It is a fact that the \(\sigma\)MA attribute is not very effective for discriminating between earthquake and landslide events, but it does work for noise detection. Comparing the three classes of MA function in Figure 5, the similar hump-shaped evolutions of earthquake and landslide would result in the close standard deviation values, which represent the similar discrete levels. On the other hand, the evolution of MA function for the noise case shows a horizontal line with small noisy disturbance, which means a lower discrete level and lower standard deviation values.