Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment
AbstractReservoir impoundment is the main factor inducing reservoir landslides, it is essential to obtain the dynamic landslide susceptibility by considering hydraulic factors. In this paper, we proposed the use of the original and revised logistic regression models (machine learning methods) to ana...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2302563 |
| _version_ | 1797336295599505408 |
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| author | Rong-jie He Nan Jiang Kun Zhang Liang Zhao Jia-wen Zhou |
| author_facet | Rong-jie He Nan Jiang Kun Zhang Liang Zhao Jia-wen Zhou |
| author_sort | Rong-jie He |
| collection | DOAJ |
| description | AbstractReservoir impoundment is the main factor inducing reservoir landslides, it is essential to obtain the dynamic landslide susceptibility by considering hydraulic factors. In this paper, we proposed the use of the original and revised logistic regression models (machine learning methods) to analyze the landslide susceptibility after the second stage of reservoir impoundment. Data of active unstable slopes before the first stage of reservoir impoundment were used to train the models. The results indicated that the two models both performed well with high accuracy. The revised logistic regression model performed better than the original logistic regression model in two aspects. Firstly, TPR and AUC of the revised model is 0.771 and 0.906 which is obviously higher than the original model of 0.743 and 0.896. Secondly, the revised model can predict the unstable slopes after reservoir impoundment which the original model can’t predict. The former could be used to obtain the dynamic landslide susceptibility in reservoir areas after different stages of reservoir impoundment and predict the potential reactivated unstable slopes more precisely due to hydraulic factors. |
| first_indexed | 2024-03-08T08:51:15Z |
| format | Article |
| id | doaj.art-5e300330390d478dafd2476fd39fe0eb |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-03-08T08:51:15Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-5e300330390d478dafd2476fd39fe0eb2024-02-01T08:41:32ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2302563Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundmentRong-jie He0Nan Jiang1Kun Zhang2Liang Zhao3Jia-wen Zhou4State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, PR ChinaCollege of Water Resources and Hydropower, Sichuan University, Chengdu, PR ChinaPower Construction Corporation of China, Power China Chengdu Engineering Corporation Limited, Chengdu, PR ChinaPower Construction Corporation of China, Power China Chengdu Engineering Corporation Limited, Chengdu, PR ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, PR ChinaAbstractReservoir impoundment is the main factor inducing reservoir landslides, it is essential to obtain the dynamic landslide susceptibility by considering hydraulic factors. In this paper, we proposed the use of the original and revised logistic regression models (machine learning methods) to analyze the landslide susceptibility after the second stage of reservoir impoundment. Data of active unstable slopes before the first stage of reservoir impoundment were used to train the models. The results indicated that the two models both performed well with high accuracy. The revised logistic regression model performed better than the original logistic regression model in two aspects. Firstly, TPR and AUC of the revised model is 0.771 and 0.906 which is obviously higher than the original model of 0.743 and 0.896. Secondly, the revised model can predict the unstable slopes after reservoir impoundment which the original model can’t predict. The former could be used to obtain the dynamic landslide susceptibility in reservoir areas after different stages of reservoir impoundment and predict the potential reactivated unstable slopes more precisely due to hydraulic factors.https://www.tandfonline.com/doi/10.1080/19475705.2024.2302563Reservoir landslidessusceptibility assessmentreservoir impoundmentmachine learningrevised logistic regression |
| spellingShingle | Rong-jie He Nan Jiang Kun Zhang Liang Zhao Jia-wen Zhou Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment Geomatics, Natural Hazards & Risk Reservoir landslides susceptibility assessment reservoir impoundment machine learning revised logistic regression |
| title | Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| title_full | Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| title_fullStr | Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| title_full_unstemmed | Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| title_short | Susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| title_sort | susceptibility assessment method for reservoir landslides considering the effect of reservoir impoundment |
| topic | Reservoir landslides susceptibility assessment reservoir impoundment machine learning revised logistic regression |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2302563 |
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