Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models
A submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork. In this research, the main objective was to analyze the potential of unsupervised m...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10544 |
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| author | Xing Du Yongfu Sun Yupeng Song Zongxiang Xiu Zhiming Su |
| author_facet | Xing Du Yongfu Sun Yupeng Song Zongxiang Xiu Zhiming Su |
| author_sort | Xing Du |
| collection | DOAJ |
| description | A submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork. In this research, the main objective was to analyze the potential of unsupervised machine learning methods and compare the performance of three different unsupervised machine learning models (k-means, spectral clustering, and hierarchical clustering) in modeling the susceptibility of the submarine landslide. Nine groups of geological factors were selected as the input parameters, which were obtained through field surveys. To estimate submarine landslide susceptibility, all input factors were separated into three or four groups based on data features and environmental variables. Finally, the goodness-of-fit and accuracy of models were validated with both internal metrics (Calinski–Harabasz index, silhouette index, and Davies–Bouldin index) and external metrics (existing landslide distribution, hydrodynamic distribution, and liquefication distribution). The findings of k-means, spectral clustering, and hierarchical clustering performed commendably and accurately in forecasting the submarine landslide susceptibility. Spectral clustering has the greatest congruence with environmental geology parameters. Therefore, the unsupervised machine learning model can be used in submarine-landslide-predicting studies, and the spectral clustering method performed best. Furthermore, machine learning can improve submarine landslide mapping in the future with the development of models and the extension of geological data related to submarine landslides. |
| first_indexed | 2024-03-09T20:45:27Z |
| format | Article |
| id | doaj.art-323539abe09e41f0856edcb8174fbfc0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:45:27Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-323539abe09e41f0856edcb8174fbfc02023-11-23T22:46:24ZengMDPI AGApplied Sciences2076-34172022-10-0112201054410.3390/app122010544Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning ModelsXing Du0Yongfu Sun1Yupeng Song2Zongxiang Xiu3Zhiming Su4First Institute of Oceanography, MNR, Qingdao 266061, ChinaNational Deep Sea Center, Qingdao 266237, ChinaFirst Institute of Oceanography, MNR, Qingdao 266061, ChinaFirst Institute of Oceanography, MNR, Qingdao 266061, ChinaFirst Institute of Oceanography, MNR, Qingdao 266061, ChinaA submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork. In this research, the main objective was to analyze the potential of unsupervised machine learning methods and compare the performance of three different unsupervised machine learning models (k-means, spectral clustering, and hierarchical clustering) in modeling the susceptibility of the submarine landslide. Nine groups of geological factors were selected as the input parameters, which were obtained through field surveys. To estimate submarine landslide susceptibility, all input factors were separated into three or four groups based on data features and environmental variables. Finally, the goodness-of-fit and accuracy of models were validated with both internal metrics (Calinski–Harabasz index, silhouette index, and Davies–Bouldin index) and external metrics (existing landslide distribution, hydrodynamic distribution, and liquefication distribution). The findings of k-means, spectral clustering, and hierarchical clustering performed commendably and accurately in forecasting the submarine landslide susceptibility. Spectral clustering has the greatest congruence with environmental geology parameters. Therefore, the unsupervised machine learning model can be used in submarine-landslide-predicting studies, and the spectral clustering method performed best. Furthermore, machine learning can improve submarine landslide mapping in the future with the development of models and the extension of geological data related to submarine landslides.https://www.mdpi.com/2076-3417/12/20/10544submarine landslidemachine learninghazard susceptibilityspatial distribution |
| spellingShingle | Xing Du Yongfu Sun Yupeng Song Zongxiang Xiu Zhiming Su Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models Applied Sciences submarine landslide machine learning hazard susceptibility spatial distribution |
| title | Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models |
| title_full | Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models |
| title_fullStr | Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models |
| title_full_unstemmed | Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models |
| title_short | Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models |
| title_sort | submarine landslide susceptibility and spatial distribution using different unsupervised machine learning models |
| topic | submarine landslide machine learning hazard susceptibility spatial distribution |
| url | https://www.mdpi.com/2076-3417/12/20/10544 |
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