三峡库区万州区滑坡易发性演化规律

肖婷; 刘庆丽; 邓敏; 刘晓东

doi:10.3799/dqkx.2024.038

Volume 50 Issue 4

Apr. 2025

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Xiao Ting, Liu Qingli, Deng Min, Liu Xiaodong, 2025. Evolution Patterns of Landslide Susceptibility in Three Gorges Reservoir Areas. Earth Science, 50(4): 1625-1637. doi: 10.3799/dqkx.2024.038

Citation:

Xiao Ting, Liu Qingli, Deng Min, Liu Xiaodong, 2025. Evolution Patterns of Landslide Susceptibility in Three Gorges Reservoir Areas. Earth Science, 50(4): 1625-1637. doi: 10.3799/dqkx.2024.038

Xiao Ting, Liu Qingli, Deng Min, Liu Xiaodong, 2025. Evolution Patterns of Landslide Susceptibility in Three Gorges Reservoir Areas. Earth Science, 50(4): 1625-1637. doi: 10.3799/dqkx.2024.038

Citation:

Xiao Ting, Liu Qingli, Deng Min, Liu Xiaodong, 2025. Evolution Patterns of Landslide Susceptibility in Three Gorges Reservoir Areas. Earth Science, 50(4): 1625-1637. doi: 10.3799/dqkx.2024.038

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Evolution Patterns of Landslide Susceptibility in Three Gorges Reservoir Areas

doi: 10.3799/dqkx.2024.038

Xiao Ting^{1, 2, 3
,
,},
Liu Qingli⁴,
Deng Min^{1, 2, 3},
Liu Xiaodong^{1, 2, 3}

1.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China
2.
Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, China
3.
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
4.
Chongqing Wanzhou Institute of Geological Environment Monitoring, Chongqing 404100, China

Received Date: 2023-12-04
Available Online: 2025-05-10
Publish Date: 2025-04-25

Abstract

Abstract

To understand the evolution of landslide susceptibility in reservoir areas, in this research it focuses on multi-temporal landslides in the Wanzhou district of the Three Gorges Reservoir area. The spatial distribution of landslides and their temporal variations were analyzed using the frequency ratio method. Machine learning algorithms are employed to construct landslide susceptibility models for different time sequences, investigating the models' temporal effectiveness and the evolution of susceptibility. The distribution trends of areas with high susceptibility in various time sequences are depicted using standard deviation ellipses. Results show that the impact of different predisposing factors on landslides changes over time. As the time span of landslide inventory data increases, the accuracy of the susceptibility models decreases. Models based on chronologically ordered data have high modeling accuracy but low predictive precision, and their predictive performance diminishes over time. The standard deviation ellipses for high-susceptibility areas differ significantly across time sequences and correlate with human engineering activities. The study highlights the temporal variability and evolving patterns of landslide susceptibility, underscoring the need for future landslide susceptibility assessments to consider the temporal aspect of landslide development.
- landslide susceptibility,
- multi-temporal landslide inventories,
- spatial and temporal distribution,
- machine learning,
- evolution law,
- engineering geology

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References(43)

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