Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
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 effici...
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3670 |
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| author | Guan-Wei Lin Ching Hung Yi-Feng Chang Chien Chung-Ray Chu Che-Hsin Liu Chih-Hsin Chang Hongey Chen |
| author_facet | Guan-Wei Lin Ching Hung Yi-Feng Chang Chien Chung-Ray Chu Che-Hsin Liu Chih-Hsin Chang Hongey Chen |
| author_sort | Guan-Wei Lin |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T19:35:51Z |
| format | Article |
| id | doaj.art-9e64e59cfcdb4936bb745add01d9c805 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:35:51Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9e64e59cfcdb4936bb745add01d9c8052023-11-20T01:45:01ZengMDPI AGApplied Sciences2076-34172020-05-011011367010.3390/app10113670Towards Automatic Landslide-Quake Identification Using a Random Forest ClassifierGuan-Wei Lin0Ching Hung1Yi-Feng Chang Chien2Chung-Ray Chu3Che-Hsin Liu4Chih-Hsin Chang5Hongey Chen6Department of Earth Sciences, National Cheng Kung University, No. 1, University Road, Tainan 701, TaiwanDepartment of Civil Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, TaiwanDepartment of Earth Sciences, National Cheng Kung University, No. 1, University Road, Tainan 701, TaiwanNational Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei 23143, TaiwanNational Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei 23143, TaiwanNational Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei 23143, TaiwanNational Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei 23143, TaiwanLandslide-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%.https://www.mdpi.com/2076-3417/10/11/3670landslide-quakebroadband array in Taiwan for seismologymachine learning |
| spellingShingle | Guan-Wei Lin Ching Hung Yi-Feng Chang Chien Chung-Ray Chu Che-Hsin Liu Chih-Hsin Chang Hongey Chen Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier Applied Sciences landslide-quake broadband array in Taiwan for seismology machine learning |
| title | Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier |
| title_full | Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier |
| title_fullStr | Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier |
| title_full_unstemmed | Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier |
| title_short | Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier |
| title_sort | towards automatic landslide quake identification using a random forest classifier |
| topic | landslide-quake broadband array in Taiwan for seismology machine learning |
| url | https://www.mdpi.com/2076-3417/10/11/3670 |
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