Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods
The geological and environmental background conditions of the Qingjiang River Basin are highly complex, particularly with frequent geological disasters along the Qingjiang reservoir bank. Previous susceptibility assessment for geological disasters was mostly focused on administrative areas and seldo...
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| Format: | Article |
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Editorial Office of The Chinese Journal of Geological Hazard and Control
2023-08-01
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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.1003-8035.202205044 |
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| author | Bin ZENG Quanru LYU Lei KOU Dong AI Huiyuan XU Jingjing YUAN |
| author_facet | Bin ZENG Quanru LYU Lei KOU Dong AI Huiyuan XU Jingjing YUAN |
| author_sort | Bin ZENG |
| collection | DOAJ |
| description | The geological and environmental background conditions of the Qingjiang River Basin are highly complex, particularly with frequent geological disasters along the Qingjiang reservoir bank. Previous susceptibility assessment for geological disasters was mostly focused on administrative areas and seldom specialized evaluations for the reservoir bank zone. Furthermore, there is still room for improvement in the evaluation index system, as well as in the pertinence and reliability of the evaluation method. To address these shortcomings, a more suitable susceptibility evaluation index system was constructed to obtain accurate and applicable susceptibility zoning results. The Yuxiakou to Ziqiu section of the Qingjiang River Basin was chosen as the research area, with the wading slope body on both sides of the river selected as the research object and the slope unit chosen as the evaluation unit. A susceptibility evaluation system composed of ten indicators, including slope, aspect, elevation range, slope type, NDVI, TWI, slope structure type, engineering geological rock formation, accumulation thickness, and valley evolution, was constructed.The logistic regression and random forest methods were used to construct the evaluation model based on the normalized certain factors, and different susceptibility zoning results were obtained. According to the evaluation results, the high-prone areas were mainly distributed in the middle to lower water wading areas of the left bank from the east of Yuxiakou to the east of Ziqiu, along the main stream of the Qingjiang River. The logistic regression model showed better applicability in the reservoir-bank section with complex topography and landforms. The research revealed that the accumulation thickness and valley evolution indicators were effective in representing the unique geological background conditions of the Qingjiang reservoir bank. The logistic regression model was able to learn the developmental law of disasters and has a reliable susceptibility prediction ability. |
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| issn | 1003-8035 |
| language | zho |
| last_indexed | 2024-03-11T14:18:34Z |
| publishDate | 2023-08-01 |
| publisher | Editorial Office of The Chinese Journal of Geological Hazard and Control |
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| series | Zhongguo dizhi zaihai yu fangzhi xuebao |
| spelling | doaj.art-cf93ca4eea964db59bcdf1b5d1d20a202023-11-01T02:11:05ZzhoEditorial Office of The Chinese Journal of Geological Hazard and ControlZhongguo dizhi zaihai yu fangzhi xuebao1003-80352023-08-0134410511310.16031/j.cnki.issn.1003-8035.202205044202205044Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methodsBin ZENG0Quanru LYU1Lei KOU2Dong AI3Huiyuan XU4Jingjing YUAN5School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430000, ChinaPower China Hubei Electric Engineering CO. Ltd., Wuhan, Hubei 430040, ChinaThe Seventh Geological Brigade of Hubei Geological Bureau, Yichang, Hubei 443000, ChinaThe Seventh Geological Brigade of Hubei Geological Bureau, Yichang, Hubei 443000, ChinaThe Seventh Geological Brigade of Hubei Geological Bureau, Yichang, Hubei 443000, ChinaThe Seventh Geological Brigade of Hubei Geological Bureau, Yichang, Hubei 443000, ChinaThe geological and environmental background conditions of the Qingjiang River Basin are highly complex, particularly with frequent geological disasters along the Qingjiang reservoir bank. Previous susceptibility assessment for geological disasters was mostly focused on administrative areas and seldom specialized evaluations for the reservoir bank zone. Furthermore, there is still room for improvement in the evaluation index system, as well as in the pertinence and reliability of the evaluation method. To address these shortcomings, a more suitable susceptibility evaluation index system was constructed to obtain accurate and applicable susceptibility zoning results. The Yuxiakou to Ziqiu section of the Qingjiang River Basin was chosen as the research area, with the wading slope body on both sides of the river selected as the research object and the slope unit chosen as the evaluation unit. A susceptibility evaluation system composed of ten indicators, including slope, aspect, elevation range, slope type, NDVI, TWI, slope structure type, engineering geological rock formation, accumulation thickness, and valley evolution, was constructed.The logistic regression and random forest methods were used to construct the evaluation model based on the normalized certain factors, and different susceptibility zoning results were obtained. According to the evaluation results, the high-prone areas were mainly distributed in the middle to lower water wading areas of the left bank from the east of Yuxiakou to the east of Ziqiu, along the main stream of the Qingjiang River. The logistic regression model showed better applicability in the reservoir-bank section with complex topography and landforms. The research revealed that the accumulation thickness and valley evolution indicators were effective in representing the unique geological background conditions of the Qingjiang reservoir bank. The logistic regression model was able to learn the developmental law of disasters and has a reliable susceptibility prediction ability.https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202205044accumulation thicknesslogistic regressionrandom forestsusceptibility assessment |
| spellingShingle | Bin ZENG Quanru LYU Lei KOU Dong AI Huiyuan XU Jingjing YUAN Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods Zhongguo dizhi zaihai yu fangzhi xuebao accumulation thickness logistic regression random forest susceptibility assessment |
| title | Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods |
| title_full | Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods |
| title_fullStr | Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods |
| title_full_unstemmed | Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods |
| title_short | Susceptibility assessment of colluvium landslides along the Changyang section of Qingjiang River using Logistic regression and random forest methods |
| title_sort | susceptibility assessment of colluvium landslides along the changyang section of qingjiang river using logistic regression and random forest methods |
| topic | accumulation thickness logistic regression random forest susceptibility assessment |
| url | https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202205044 |
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