TY - JOUR
T1 - Towards automatic landslide-quake identification using a random forest classifier
AU - Lin, Guan Wei
AU - Hung, Ching
AU - Chien, Yi Feng Chang
AU - Chu, Chung Ray
AU - Liu, Che Hsin
AU - Chang, Chih Hsin
AU - Chen, Hongey
N1 - Funding Information:
Funding: This research was funded by Soil and Water Conservation Bureau, Council of Agriculture (COA), Executive Yuan of Taiwan.
Funding Information:
Acknowledgments: 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.
Funding Information:
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, CentralWeather 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.
Publisher Copyright:
© 2020 by the authors.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Landslide-generated seismic waves (landslide-quakes), exhibiting distinctive waveforms and frequency characteristics, can be recorded by nearby seismometers. Implementing an automatic classifier for landslide-quakes could help provide objective and accurate initiation times of landslides with efficiency. This study collected and analyzed the time information of 214 landslide seismic records due to 33 documented landslide events, from the Broadband Array in Taiwan for Seismology (BATS). In addition, equal numbers of earthquake and noise signals were also incorporated. The 642 seismic signals and time information were carefully examined using the random forest algorithm to create an automatic landslide-quake classifier. By validating the signal attributes of the landslide, earthquake, and noise events, specifically in the time and frequency domains, it was shown that the proposed classifier can reach an accuracy (the proportion of all correctly classified events to the total number of events) of 91.3%. To further evaluate the applicability of the automatic classifier, landslide-quakes generated during the devastating Typhoon Morakot (2009) and Typhoon Soudelor (2015) were also verified, showing that the sensitivity of the classifier is higher than 98%.
AB - Landslide-generated seismic waves (landslide-quakes), exhibiting distinctive waveforms and frequency characteristics, can be recorded by nearby seismometers. Implementing an automatic classifier for landslide-quakes could help provide objective and accurate initiation times of landslides with efficiency. This study collected and analyzed the time information of 214 landslide seismic records due to 33 documented landslide events, from the Broadband Array in Taiwan for Seismology (BATS). In addition, equal numbers of earthquake and noise signals were also incorporated. The 642 seismic signals and time information were carefully examined using the random forest algorithm to create an automatic landslide-quake classifier. By validating the signal attributes of the landslide, earthquake, and noise events, specifically in the time and frequency domains, it was shown that the proposed classifier can reach an accuracy (the proportion of all correctly classified events to the total number of events) of 91.3%. To further evaluate the applicability of the automatic classifier, landslide-quakes generated during the devastating Typhoon Morakot (2009) and Typhoon Soudelor (2015) were also verified, showing that the sensitivity of the classifier is higher than 98%.
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U2 - 10.3390/app10113670
DO - 10.3390/app10113670
M3 - Article
AN - SCOPUS:85086097593
SN - 2076-3417
VL - 10
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 11
M1 - 3670
ER -