Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network
The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of The Chinese Journal of Geological Hazard and Control
2023-12-01
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| Series: | Zhongguo dizhi zaihai yu fangzhi xuebao |
| Subjects: | |
| Online Access: | https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202303062 |
| _version_ | 1797303299605528576 |
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| author | Ziyan GAO Ruidong LI Pengqing SHI Xiaolong ZHOU Juan ZHANG |
| author_facet | Ziyan GAO Ruidong LI Pengqing SHI Xiaolong ZHOU Juan ZHANG |
| author_sort | Ziyan GAO |
| collection | DOAJ |
| description | The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error , mean absolute error , coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie. |
| first_indexed | 2024-03-07T23:50:54Z |
| format | Article |
| id | doaj.art-3810bf03383e43ac8ba247ba7eb12e7d |
| institution | Directory Open Access Journal |
| issn | 1003-8035 |
| language | zho |
| last_indexed | 2024-03-07T23:50:54Z |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of The Chinese Journal of Geological Hazard and Control |
| record_format | Article |
| series | Zhongguo dizhi zaihai yu fangzhi xuebao |
| spelling | doaj.art-3810bf03383e43ac8ba247ba7eb12e7d2024-02-19T07:14:15ZzhoEditorial Office of The Chinese Journal of Geological Hazard and ControlZhongguo dizhi zaihai yu fangzhi xuebao1003-80352023-12-01346303610.16031/j.cnki.issn.1003-8035.202303062202303062Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory networkZiyan GAO0Ruidong LI1Pengqing SHI2Xiaolong ZHOU3Juan ZHANG4Key Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu 730050, ChinaKey Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu 730050, ChinaKey Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu 730050, ChinaKey Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu 730050, ChinaKey Laboratory of Groundwater Engineering and Geothermal Resources in Gansu Province, Lanzhou, Gansu 730050, ChinaThe North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error , mean absolute error , coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie.https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202303062landslidelstm neural networkpredictive analysisnorth mountain of lijiemachine learning |
| spellingShingle | Ziyan GAO Ruidong LI Pengqing SHI Xiaolong ZHOU Juan ZHANG Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network Zhongguo dizhi zaihai yu fangzhi xuebao landslide lstm neural network predictive analysis north mountain of lijie machine learning |
| title | Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network |
| title_full | Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network |
| title_fullStr | Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network |
| title_full_unstemmed | Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network |
| title_short | Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network |
| title_sort | deformation prediction of the northern mountain landslide in lijie town of zhouqu gansu province based on long short term memory network |
| topic | landslide lstm neural network predictive analysis north mountain of lijie machine learning |
| url | https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202303062 |
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