Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining
In order to explore the association criteria of landslide multi-field monitoring data, we have adopted the two-step clustering method and Apriori algorithm, which belong to the classical data mining method, and we have also proposed the process of landslide monitoring data mining. Based on the Baish...
| 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
2021-12-01
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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.1003-8035.2021.06-01 |
| _version_ | 1797865872314859520 |
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| author | Rui CHEN Xiaoguang FAN Yiping WU |
| author_facet | Rui CHEN Xiaoguang FAN Yiping WU |
| author_sort | Rui CHEN |
| collection | DOAJ |
| description | In order to explore the association criteria of landslide multi-field monitoring data, we have adopted the two-step clustering method and Apriori algorithm, which belong to the classical data mining method, and we have also proposed the process of landslide monitoring data mining. Based on the Baishuihe landslide in the Three Gorges Reservoir Area, we analyzed the monitoring data of ZG93 from June 2003 to June 2016. The main inducing factors of the landslide displacement were selected, and the two-step clustering method was used to pre-cluster and cluster the different influence factors. We used Apriori algorithm to deal with the classified variables to generate frequent item sets that satisfy the minimum support degree. The association rules between the precipitating factors and the landslide deformation are established under the multi-field coupling mode of Baishuihe landslide. The results show that the correlation criterion is of great significance to the deformation analysis of landslide hazards and the data mining technology can be applied to the displacement prediction of geological hazards in the Three Gorges Reservoir Area. |
| first_indexed | 2024-04-09T23:15:09Z |
| format | Article |
| id | doaj.art-333e50eba0ef4da28efba37c4ff74793 |
| institution | Directory Open Access Journal |
| issn | 1003-8035 |
| language | zho |
| last_indexed | 2024-04-09T23:15:09Z |
| publishDate | 2021-12-01 |
| 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-333e50eba0ef4da28efba37c4ff747932023-03-22T09:12:34ZzhoEditorial Office of The Chinese Journal of Geological Hazard and ControlZhongguo dizhi zaihai yu fangzhi xuebao1003-80352021-12-013261810.16031/j.cnki.issn.1003-8035.2021.06-01202012024Analysis on association rules of multi-field information of Baishuihe landslide based on the data miningRui CHEN0Xiaoguang FAN1Yiping WU2Faculty of Engineering, China University of Geosciences, Wuhan, Hubei 430074, ChinaHenan Electric Power Survey & Design Institute, Zhengzhou, Henan 450007, ChinaFaculty of Engineering, China University of Geosciences, Wuhan, Hubei 430074, ChinaIn order to explore the association criteria of landslide multi-field monitoring data, we have adopted the two-step clustering method and Apriori algorithm, which belong to the classical data mining method, and we have also proposed the process of landslide monitoring data mining. Based on the Baishuihe landslide in the Three Gorges Reservoir Area, we analyzed the monitoring data of ZG93 from June 2003 to June 2016. The main inducing factors of the landslide displacement were selected, and the two-step clustering method was used to pre-cluster and cluster the different influence factors. We used Apriori algorithm to deal with the classified variables to generate frequent item sets that satisfy the minimum support degree. The association rules between the precipitating factors and the landslide deformation are established under the multi-field coupling mode of Baishuihe landslide. The results show that the correlation criterion is of great significance to the deformation analysis of landslide hazards and the data mining technology can be applied to the displacement prediction of geological hazards in the Three Gorges Reservoir Area.https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.2021.06-01reservoir landslidedata miningtwo-step clustering methodapriori algorithmassociation rules |
| spellingShingle | Rui CHEN Xiaoguang FAN Yiping WU Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining Zhongguo dizhi zaihai yu fangzhi xuebao reservoir landslide data mining two-step clustering method apriori algorithm association rules |
| title | Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining |
| title_full | Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining |
| title_fullStr | Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining |
| title_full_unstemmed | Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining |
| title_short | Analysis on association rules of multi-field information of Baishuihe landslide based on the data mining |
| title_sort | analysis on association rules of multi field information of baishuihe landslide based on the data mining |
| topic | reservoir landslide data mining two-step clustering method apriori algorithm association rules |
| url | https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.2021.06-01 |
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