The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation
Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate e...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Online Access: | https://www.mdpi.com/2073-445X/12/2/344 |
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| author | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Baoyin Liu Dongpan Chen Guansheng Qian Li Zhang Jingxuan Lu |
| author_facet | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Baoyin Liu Dongpan Chen Guansheng Qian Li Zhang Jingxuan Lu |
| author_sort | Xiangyu Li |
| collection | DOAJ |
| description | Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability. |
| first_indexed | 2024-03-11T08:34:05Z |
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| id | doaj.art-e693727d9a79472580477a2702a5fbea |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-11T08:34:05Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj.art-e693727d9a79472580477a2702a5fbea2023-11-16T21:36:02ZengMDPI AGLand2073-445X2023-01-0112234410.3390/land12020344The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal SegmentationXiangyu Li0Tianjie Lei1Jing Qin2Jiabao Wang3Weiwei Wang4Baoyin Liu5Dongpan Chen6Guansheng Qian7Li Zhang8Jingxuan Lu9State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing (CUMTB), Beijing 100083, ChinaChina Electronic Greatwall ShengFeiFan Information System Co., Ltd., Beijing 102200, ChinaInstitutes of Science and Development, University of Chinese Academy of Sciences, Beijing 100190, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaBeijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaMost slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability.https://www.mdpi.com/2073-445X/12/2/344fisher optimal segmentation methodwarningthreshold determinationregression model |
| spellingShingle | Xiangyu Li Tianjie Lei Jing Qin Jiabao Wang Weiwei Wang Baoyin Liu Dongpan Chen Guansheng Qian Li Zhang Jingxuan Lu The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation Land fisher optimal segmentation method warning threshold determination regression model |
| title | The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation |
| title_full | The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation |
| title_fullStr | The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation |
| title_full_unstemmed | The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation |
| title_short | The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation |
| title_sort | method of segmenting the early warning thresholds based on fisher optimal segmentation |
| topic | fisher optimal segmentation method warning threshold determination regression model |
| url | https://www.mdpi.com/2073-445X/12/2/344 |
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