考虑非滑坡样本选取和集成机器学习方法的水库滑坡易发性预测

王悦; 曹颖; 许方党; 周超; 余蓝冰; 吴立星; 汪洋; 殷坤龙

doi:10.3799/dqkx.2022.407

Volume 49 Issue 5

May 2024

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Article Navigation > Earth Science > 2024 > 49(5): 1619-1635

Wang Yue, Cao Ying, Xu Fangdang, Zhou Chao, Yu Lanbing, Wu Lixing, Wang Yang, Yin Kunlong, 2024. Reservoir Landslide Susceptibility Prediction Considering Non-Landslide Sampling and Ensemble Machine Learning Methods. Earth Science, 49(5): 1619-1635. doi: 10.3799/dqkx.2022.407

Citation:

Wang Yue, Cao Ying, Xu Fangdang, Zhou Chao, Yu Lanbing, Wu Lixing, Wang Yang, Yin Kunlong, 2024. Reservoir Landslide Susceptibility Prediction Considering Non-Landslide Sampling and Ensemble Machine Learning Methods. Earth Science, 49(5): 1619-1635. doi: 10.3799/dqkx.2022.407

Citation:

Wang Yue, Cao Ying, Xu Fangdang, Zhou Chao, Yu Lanbing, Wu Lixing, Wang Yang, Yin Kunlong, 2024. Reservoir Landslide Susceptibility Prediction Considering Non-Landslide Sampling and Ensemble Machine Learning Methods. Earth Science, 49(5): 1619-1635. doi: 10.3799/dqkx.2022.407

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Reservoir Landslide Susceptibility Prediction Considering Non-Landslide Sampling and Ensemble Machine Learning Methods

doi: 10.3799/dqkx.2022.407

Wang Yue^{1
,
,},
Cao Ying^{1, 3},
Xu Fangdang⁴,
Zhou Chao^{2, 3
,
,},
Yu Lanbing¹,
Wu Lixing¹,
Wang Yang^{1, 3},
Yin Kunlong^{1, 3}

1.
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
2.
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
3.
Research Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir, Chongqing 404199, China
4.
11th Geological Team of Zhejiang Province, Wenzhou 325006, China

Received Date: 2022-11-08
Available Online: 2024-06-04
Publish Date: 2024-05-25

Abstract

Abstract

Landslide susceptibility evaluation is important for its early warning and forecasting and risk management.To address the problems of a random selection of non-landslide samples and low accuracy of individual classifiers in modeling by machine learning techniques, a coupled multi-model regional landslide susceptibility modeling framework is proposed.Taking the Zigui-Badong section of the Three Gorges reservoir area as an example, 12 factors such as elevation and slope were selected to construct an evaluation index system, and the information quantity method was applied to quantify the influence degree of each factor on landslide spatial development. 70% of the landslides were randomly selected as training samples and the remaining 30% as validation samples; the Logistic Regression model (LR) was applied to produce an initial susceptibility zoning map of the study area and to determine the constraint range for random sampling of non-landslides. Subsequently, a single Classification and Regression Tree (LR-CART and No-CART) and combined Classification and Regression Tree-Bagging model (LR-CART-Bagging and No-CART-Bagging) were applied to model landslide susceptibility using randomly selected non-landslide samples under the constrained and unconstrained conditions of LR model, respectively, and multiple metrics were applied for accuracy assessment.The results show that elevation and water system are the main controlling factors for landslide development; the accuracy of the LR-CART-Bagging model is 0.973, higher than 0.889 of the LR-CART model; compared with No-CART and No-CART-Bagging models, the accuracy of LR-CART and LR-CART-Bagging models is improved by 0.057 and 0.047, respectively.LR model can effectively constrain the selection range of non-landslide samples and improve the quality of sample selection; the CART-Bagging model integrates the advantages of machine learning and ensemble learning with better prediction performance, and the proposed LR-CART-Bagging model is an accurate and reliable method for landslide susceptibility modeling.
- machine learning,
- landslides,
- susceptibility mapping,
- non-landslide sampling,
- ensemble learning,
- Three Gorges reservoir area,
- engineering geology

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References(59)

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