# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 5 Conclusions  
In this study, an automatic classifier using a random forest supervised algorithm with a training dataset of 642 seismic signals was built to classify three kinds of events: landslides, earthquakes, and noise. To train the model, 24 attributes in the time and frequency domains were computed and integrated. The present classifier was validated by the training dataset using five-fold cross validation, and the excellent accuracy of 91.3% supported that this methodology can discriminate the characteristics of the three kinds of events. The sensitivities and precision for landslide cases are 86.9% and 89.0%, respectively. Tests of single-attribute classifiers were performed to clarify the importance of each attribute. We concluded that the influence on the classification result was positive if the accuracy of a single-attribute classifier exceeded 50%, and the influences of the time-domain attributes as a whole were more important than those in the frequency domain because the former had better discrimination results for the cases of landslides and noise. Generally, the time-domain attributes had better discriminant ability for the cases of landslides and noise, and the frequency-domain attributes were better for discriminating cases of earthquakes from noise. An integrated classifier combining attributes in the domains of time and frequency together achieved better classification results for all three kinds of events. To test the practical application, two datasets collected during different typhoon events were processed by the established automatic classifier. For data collected during Typhoon Morakot, of a total of 193 landslide events identified by human operators, 191 landslide events were correctly classified, one was misclassified as an earthquake event, and one was not detected. For data collected during Typhoon Soudelor, all 17 landslide events were classified successfully, in agreement with the human operator results. The very high sensitivity of 98% demonstrated that the present automatic classifier using the random forest supervised algorithm is feasible, for it can provide more precise occurrence times of landslides for historical events and presents the potential for implementation of a nearly real-time alarm system for large landslides.  
Conceptualization: G.-W.L., C.H., and H.C.; methodology: G.-W.L. and Y.-F.C.C.; validation: G.-W.L., Y.-F.C.C., and C.-R.C.; formal analysis: G.-W.L. and Y.-F.C.C.; writing--original draft preparation: [PERSON]-[PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON], and [PERSON]; supervision: [PERSON] and H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Soil and Water Conservation Bureau, Council of Agriculture (COA), Executive Yuan of Taiwan.  
The authors gratefully acknowledged financial supports from the Ministry of Science and Technology of Taiwan, and the Soil and Water Conservation Bureau, Council of Agriculture, Executive Yuan of Taiwan. The source of all seismic and rainfall information included in this paper was from the Institute of Earth Sciences, Academia Sinica of Taiwan and the Seismology Center, Central Weather Bureau (CWB), Taiwan. The research was, in part, supported by the Higher Education Sprout Project, Ministry of Education, Taiwan, Headquarters of University Advancement to the National Cheng Kung University, Taiwan.  
The authors declare no conflict of interest.