Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China)
There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-01-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2018.1478892 |
| _version_ | 1798044836603887616 |
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| author | Junqi Liu Huiming Tang Qi Li Aijun Su Qianhui Liu Cheng Zhong |
| author_facet | Junqi Liu Huiming Tang Qi Li Aijun Su Qianhui Liu Cheng Zhong |
| author_sort | Junqi Liu |
| collection | DOAJ |
| description | There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect. |
| first_indexed | 2024-04-11T23:10:44Z |
| format | Article |
| id | doaj.art-4672bdd0ab494d1b8685b9b8bff382dc |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-04-11T23:10:44Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-4672bdd0ab494d1b8685b9b8bff382dc2022-12-22T03:57:50ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132018-01-019188189110.1080/19475705.2018.14788921478892Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China)Junqi Liu0Huiming Tang1Qi Li2Aijun Su3Qianhui Liu4Cheng Zhong5China University of GeosciencesChina University of GeosciencesInstitute of Rock and Soil Mechanics, Chinese Academy of SciencesChina University of GeosciencesPeking UniversityChina University of GeosciencesThere hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect.http://dx.doi.org/10.1080/19475705.2018.1478892Landslide monitoringBig Datamathematical statisticsneural networkdata fusionthe Three Gorges Reservoir |
| spellingShingle | Junqi Liu Huiming Tang Qi Li Aijun Su Qianhui Liu Cheng Zhong Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) Geomatics, Natural Hazards & Risk Landslide monitoring Big Data mathematical statistics neural network data fusion the Three Gorges Reservoir |
| title | Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) |
| title_full | Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) |
| title_fullStr | Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) |
| title_full_unstemmed | Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) |
| title_short | Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China) |
| title_sort | multi sensor fusion of data for monitoring of huangtupo landslide in the three gorges reservoir china |
| topic | Landslide monitoring Big Data mathematical statistics neural network data fusion the Three Gorges Reservoir |
| url | http://dx.doi.org/10.1080/19475705.2018.1478892 |
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