State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network
Identifying the states of hydrodynamic pressure-driven landslides can more effectively assist in the analysis of the landslide deformation law, and accurately identifying the landslide state is of great significance for the in-depth study of the hydrodynamic pressure-driven landslide state. Aiming a...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Bulletin of Geological Science and Technology
2022-11-01
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| Series: | 地质科技通报 |
| Subjects: | |
| Online Access: | https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.2022.0215 |
| _version_ | 1797277582886961152 |
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| author | Qingjie Xu Yong Liu Weiwen Zhan Jingkai Guo Xingrui Li |
| author_facet | Qingjie Xu Yong Liu Weiwen Zhan Jingkai Guo Xingrui Li |
| author_sort | Qingjie Xu |
| collection | DOAJ |
| description | Identifying the states of hydrodynamic pressure-driven landslides can more effectively assist in the analysis of the landslide deformation law, and accurately identifying the landslide state is of great significance for the in-depth study of the hydrodynamic pressure-driven landslide state. Aiming at the problem that there are few abrupt states of hydrodynamic pressure-driven landslides, it is difficult to obtain relevant features, which leads to poor state recognition performance, and a generative adversarial network learning method for landslide state recognition is proposed.In this method, the landslide state monitoring data matrix is constructed, a reasonable generator network is designed based on a small number of data samples to complete the data amplification of the landslide states, and the discriminator network is designed to realize the screening of the amplified data, and the classifying landslide states being realized through the confrontation generation network to achieve the purpose of landslide status identification. Taking the Baishuihe landslide in the Three Gorges Reservoir area as the research object, multisource monitoring data such as rainfall, reservoir water level, deep displacement, and surface displacement are normalized. The states recognition generative adversarial network completes the classification and identification of the landslide state. The results show that the generative adversarial network has high accuracy for landslide state recognition. The research method in this paper can accurately identify and classify the landslide state in the target area. |
| first_indexed | 2024-03-07T15:50:30Z |
| format | Article |
| id | doaj.art-9483e92a79c747a1a742115eecb47b6e |
| institution | Directory Open Access Journal |
| issn | 2096-8523 |
| language | zho |
| last_indexed | 2024-03-07T15:50:30Z |
| publishDate | 2022-11-01 |
| publisher | Editorial Department of Bulletin of Geological Science and Technology |
| record_format | Article |
| series | 地质科技通报 |
| spelling | doaj.art-9483e92a79c747a1a742115eecb47b6e2024-03-05T02:57:40ZzhoEditorial Department of Bulletin of Geological Science and Technology地质科技通报2096-85232022-11-0141612913610.19509/j.cnki.dzkq.2022.0215dzkjtb-41-6-129State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial networkQingjie Xu0Yong Liu1Weiwen Zhan2Jingkai Guo3Xingrui Li4School of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan 430074, ChinaIdentifying the states of hydrodynamic pressure-driven landslides can more effectively assist in the analysis of the landslide deformation law, and accurately identifying the landslide state is of great significance for the in-depth study of the hydrodynamic pressure-driven landslide state. Aiming at the problem that there are few abrupt states of hydrodynamic pressure-driven landslides, it is difficult to obtain relevant features, which leads to poor state recognition performance, and a generative adversarial network learning method for landslide state recognition is proposed.In this method, the landslide state monitoring data matrix is constructed, a reasonable generator network is designed based on a small number of data samples to complete the data amplification of the landslide states, and the discriminator network is designed to realize the screening of the amplified data, and the classifying landslide states being realized through the confrontation generation network to achieve the purpose of landslide status identification. Taking the Baishuihe landslide in the Three Gorges Reservoir area as the research object, multisource monitoring data such as rainfall, reservoir water level, deep displacement, and surface displacement are normalized. The states recognition generative adversarial network completes the classification and identification of the landslide state. The results show that the generative adversarial network has high accuracy for landslide state recognition. The research method in this paper can accurately identify and classify the landslide state in the target area.https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.2022.0215hydrodynamic pressure-driven landslidegenerative adversarial networklandslide statebaishuihe landslide |
| spellingShingle | Qingjie Xu Yong Liu Weiwen Zhan Jingkai Guo Xingrui Li State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network 地质科技通报 hydrodynamic pressure-driven landslide generative adversarial network landslide state baishuihe landslide |
| title | State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network |
| title_full | State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network |
| title_fullStr | State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network |
| title_full_unstemmed | State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network |
| title_short | State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network |
| title_sort | state recognition method of hydrodynamic pressure driven landslides based on a generative adversarial network |
| topic | hydrodynamic pressure-driven landslide generative adversarial network landslide state baishuihe landslide |
| url | https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.2022.0215 |
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