基于水系分区的滑坡易发性机器学习分析方法: 以重庆市奉节县为例

仉文岗; 何昱苇; 王鲁琦; 刘松林; 陈柏林

doi:10.3799/dqkx.2022.309

Volume 48 Issue 5

May 2023

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2023 > 48(5): 2024-2038

Zhang Wengang, He Yuwei, Wang Luqi, Liu Songlin, Chen Bolin, 2023. Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing. Earth Science, 48(5): 2024-2038. doi: 10.3799/dqkx.2022.309

Citation:

Zhang Wengang, He Yuwei, Wang Luqi, Liu Songlin, Chen Bolin, 2023. Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing. Earth Science, 48(5): 2024-2038. doi: 10.3799/dqkx.2022.309

Citation:

Zhang Wengang, He Yuwei, Wang Luqi, Liu Songlin, Chen Bolin, 2023. Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing. Earth Science, 48(5): 2024-2038. doi: 10.3799/dqkx.2022.309

PDF( 8887 KB)

Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing

doi: 10.3799/dqkx.2022.309

Zhang Wengang^{1, 2
,
,},
He Yuwei^{1, 2},
Wang Luqi^{1, 2
,
,},
Liu Songlin^{1, 2},
Chen Bolin³

1.
School of Civil Engineering, Chongqing University, Chongqing 400045, China
2.
National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing 400045, China
3.
Chongqing Institute of Geology and Mineral Resources, Chongqing 401120, China

Received Date: 2022-07-27
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

The Three Gorges Reservoir Area is the key area for geological disaster management, and the hydraulic effect of the Yangtze River on the slopes along its banks cannot be ignored. Therefore, it is necessary to study the influence of drainage factors on landslide susceptibility. The historical landslides points in Fengjie County and their corresponding features are taken as analysis data. Due to the significant influence of regional water system, the research area is divided into two sub-zones according to hydrographic conditions. Area of 300 meters along the two sides of the rivers is regarded as Sub-Zone Ⅰ, and the remaining area is defined as Sub-Zone Ⅱ. Then, a total 16 influencing factors are selected to establish landslide susceptibility evaluating models, and the landslide susceptibility evaluation results of the whole region and sub-zones were compared and analyzed. The following results of landslide susceptibility analysis based on machine learningalgorithm can be obtained. Because the fluctuation of reservoir water level reduces the effective stress of anti-slip section and the cultivated land has weak conservation effect on slope mass owing to the destruction of the original mountain balance in the process of reclamation, the areas with high and extremely high probability of landslide occurrences in Fengjie County mainly lie on the bank of rivers and in the area of cultivated land. The accuracy of susceptibility assessment of the hydrographic-divided model is better than the whole-range model. Specifically, the accuracy and F-score are improved by 5.1% and 5.2%, which indicates the practicability and validity of conducting zone-dividing susceptibility analysis.
- landslide,
- machine learning,
- sub-zone based on hydrographic division,
- random forest,
- susceptibility assessment,
- engineering geology

FullText(HTML)

References(50)

References

Ali, S. K. A., Parvin, F., Pham, Q. B., et al., 2022. An Ensemble Random Forest Tree with SVM, ANN, NBT, and LMT for Landslide Susceptibility Mapping in the Rangit River Watershed, India. Natural Hazards, 113(3): 1601-1633. https://doi.org/10.1007/s11069-022-05360-5

Amiri, M., Pourghasemi, H. R., Ghanbarian, G. A., et al., 2019. Assessment of the Importance of Gully Erosion Effective Factors Using Boruta Algorithm and Its Spatial Modeling and Mapping Using Three Machine Learning Algorithms. Geoderma, 340: 55-69. https://doi. org/10.1016/j. geoderma. 2018.12. 042. doi: 10.1016/j.geoderma.2018.12.042

Bai, S. B., Wang, J., Lu, G. N., et al., 2010. GIS-Based Logistic Regression for Landslide Susceptibility Mapping of the Zhongxian Segment in the Three Gorges Area, China. Geomorphology, 115(1-2): 23-31. https://doi.org/10.1016/j.geomorph.2009.09.025

Basu, T., Pal, S., 2018. Identification of Landslide Susceptibility Zones in Gish River Basin, West Bengal, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12(1): 14-28. https://doi.org/10.1080/17499518.2017.1343482

Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5-32. https://doi.org/10.1023/a:1010933404324

Chen, T., Zhu, L., Niu, R. Q., et al., 2020. Mapping Landslide Susceptibility at the Three Gorges Reservoir, China, Using Gradient Boosting Decision Tree, Random Forest and Information Value Models. Journal of Mountain Science, 17(3): 670-685. https://doi.org/10.1007/s11629-019-5839-3

Das, A. M., Kumar, N. S., Kanti, M. S., 2011. Landslide Hazard and Risk Analysis in India at a Regional Scale. Disaster Advances, 4(2): 26-39. http://www.researchgate.net/publication/287631967_Landslide_Hazard_and_Risk_Analysis_in_India_at_a_Regional_Scale

Guo, Z. Z., Yin, K. L., Huang, F. M., et al., 2019. Evaluation of Landslide Susceptibility Based on Landslide Classification and Weighted Frequency Ratio Model. Chinese Journal of Rock Mechanics and Engineering, 38(2): 287-300 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-YSLX201902007.htm

Havenith, H. B., Torgoev, A., Schlogel, R., et al., 2015. Tien Shan Geohazards Database: Landslide Susceptibility Analysis. Geomorphology, 249: 32-43. https://doi.org/10.1016/j.geomorph.2015.03.019

Huang, F. M., Cao, Y., Fan, X. M., et al., 2021. Effects of Different Landslide Boundaries and Their Spatial Shapes on the Uncertainty of Landslide Susceptibility Prediction. Chinese Journal of Rock Mechanics and Engineering, 40(S2): 3227-3240 (in Chinese with English abstract).

Huang, F. M., Chen, J. W., Fan, X. M., et al., 2022. Logistic Regression Fitting of Rainfall-Induced Landslide Occurrence Probability and Continuous Landslide Hazard Prediction Modelling. Earth Science, 47(12): 4609-4628 (in Chinese with English abstract). http://www.sciencedirect.com/science/article/pii/S0169555X22001295

Huang, F. M., Wang, Y., Dong, Z. l., et al., 2019. Regional Landslide Susceptibility Mapping Based on Grey Relational Degree Model. Earth Science, 44(2): 664-676 (in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201902027.htm

Khamkar, D. J., Mhaske, S. Y., 2019. Identification of Landslide Susceptible Settlements Using Geographical Information System of Yelwandi River Basin, Maharashtra (India). Natural Hazards, 96(3): 1263-1287. https://doi.org/10.1007/s11069-019-03609-0

Li, S. L., Xu, Q., Tang, M. G., et al., 2019. Characterizing the Spatial Distribution and Fundamental Controls of Landslides in the Three Gorges Reservoir Area, China. Bulletin of Engineering Geology and the Environment, 78(6): 4275-4290. https://doi.org/10.1007/s10064-018-1404-5

Li, W. B., Fan, X. M., Huang, F. M., et al., 2021. Uncertainties of Landslide Susceptibility Modeling under Different Environmental Factor Connections and Prediction Models. Earth Science, 46(10): 3777-3795 (in Chinese with English abstract). http://www.sciencedirect.com/science/article/pii/S0341816221001090

Li, Y. W., Wang, X. M., Mao, H., 2020. Influence of Human Activity on Landslide Susceptibility Development in the Three Gorges Area. Natural Hazards, 104(3): 2115-2151. https://doi.org/10.1007/s11069-020-04264-6

Long, J. J., Liu, Y., Li, C. D., et al., 2021. A Novel Model for Regional Susceptibility Mapping of Rainfall Reservoir Induced Landslides in Jurassic Slide-Prone Strata of Western Hubei Province, Three Gorges Reservoir Area. Stochastic Environmental Research and Risk Assessment, 35(7): 1403-1426. https://doi.org/10.1007/s00477-020-01892-z

Sajadi, P., Sang, Y. F., Gholamnia, M., et al., 2022. Evaluation of the Landslide Susceptibility and Its Spatial Difference in the Whole Qinghai-Tibetan Plateau Region by Five Learning Algorithms. Geoscience Letters, 9(1): 9. https://doi.org/10.1186/s40562-022-00218-x

Shou, K. J., Chen, J. R., 2021. On the Rainfall Induced Deep-Seated and Shallow Landslide Hazard in Taiwan. Engineering Geology, 288. https://doi.org/10.1016/j.enggeo.2021.106156

Sun, D. L., Gu, Q. Y., Wen, H. J., et al., 2022. A Hybrid Landslide Warning Model Coupling Susceptibility Zoning and Precipitation. Forests, 13: 827. https://doi.org/10.3390/f13060827

Sun, D. L., Xu, J. H., Wen, H. J., et al., 2021. Assessment of Landslide Susceptibility Mapping Based on Bayesian Hyperparameter Optimization: A Comparison between Logistic Regression and Random Forest. Engineering Geology, 281. https://doi.org/10.1016/j.enggeo.2020.105972

Van Den Eeckhaut, M., Marre, A., Poesen, J., 2010. Comparison of Two Landslide Susceptibility Assessments in the Champagne-Ardenne Region (France). Geomorphology, 115(1-2): 141-55. https://doi.org/10.1016/j.geomorph.2009.09.042

Wang, J. G., Schweizer, D., Liu, Q. B., et al., 2021a. Three-Dimensional Landslide Evolution Model at the Yangtze River. Engineering Geology, 292. https://doi.org/10.1016/j.enggeo.2021.106275

Wang, L. Q., Zhang, Z. H., Huang, B. L., et al., 2021b. Triggering Mechanism and Possible Evolution Process of the Ancient Qingshi Landslide in the Three Gorges Reservoir. Geomatics, Natural Hazards and Risk, 12(1): 3160-3174. https://doi.org/10.1080/19475705.2021.1998230

Wang, M., Qiao, J. P., 2013. Reservoir-Landslide Hazard Assessment Based on Gis: A Case Study in Wanzhou Section of the Three Gorges Reservoir. Journal of Mountain Science, 10(6): 1085-1096. https://doi.org/10.1007/s11629-013-2498-7

Weidner, L., DePrekel, K., Oommen, T., et al., 2019. Investigating Large Landslides along a River Valley Using Combined Physical, Statistical, and Hydrologic Modeling. Engineering Geology, 259. https://doi.org/10.1016/j.enggeo.2019.105169

Wu, R. Z., Hu, X. D., Mei, H. B., et al., 2021. Spatial Susceptibility Assessment of Landslides Based on Random Forest: A Case Study from Hubei Section in the Three Gorges Reservoir Area. Earth Science, 46(1): 321-330 (in Chinese with English abstract).

Xiao, T., 2020. Landslide Risk Assessment in Wanzhou District and a Key Section, Three Gorges Reservoir (Dissertation). China University of Geosciences, Wuhan (in Chinese with English abstract).

Xu, X. J., Yang, Q., 2014. Study of Reservoir-Accumulative Landslide's Stability Evolution Trend in the Three Gorges Reservoir. Paper Presented at the 5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), Zhangjiajie.

Yang, B. B., Yin, K. L., Lacasse, S., et al., 2019. Time Series Analysis and Long Short-Term Memory Neural Network to Predict Landslide Displacement. Landslides, 16(4): 677-694. https://doi.org/10.1007/s10346-018-01127-x

Ye, R. Q., Li, S. Y., Guo, F., et al., 2021. RS and GIS Analysis on Relationship between Landslide Susceptibility and Land Use Change in Three Gorges Reservoir Area. Journal of Engineering Geology, 29(3): 724-733 (in Chinese with English abstract).

Yin, Y. P., Wang, L. Q., Zhao, P., et al., 2022. Crashed Failure Mechanism & Prevention of Fractured High-Steep Slope in the Three Gorges Reservoir, China. Journal of Hydraulic Engineering, 53(4): 379-391 (in Chinese with English abstract).

Yu, X. Y., Wang, Y., Niu, R. Q., et al., 2016. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China. International Journal of Environmental Research and Public Health, 13(5): 487. https://doi.org/10.3390/ijerph13050487

Zhang, H. J., Song, Y. X., Xu, S. L., et al., 2022. Combining a Class-Weighted Algorithm and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study of Wanzhou Section of the Three Gorges Reservoir, China. Computers & Geosciences, 158. https://doi.org/10.1016/j.cageo.2021.104966.

Zhang, K. Q., Wang, L. Q., Zhang, W. G., et al., 2021. Formation and Failure Mechanism of the Xinfangzi Landslide in Chongqing City (China). Applied Sciences- Basel, 11(19). https://doi.org/10.3390/app11198963

Zhou, C., Yin, K. L., Cao, Y., et al., 2018. Landslide Susceptibility Modeling Applying Machine Learning Methods: A Case Study from Longju in the Three Gorges Reservoir Area, China. Computers & Geosciences, 112: 23-37. https://doi.org/10.1016/j.cageo.2017.11.019

Zhou, X. T., Huang, F. M., Wu, W. C., et al., 2022. Regional Landslide Susceptibility Prediction Based on Negative Sample Selected by Coupling Information Value Method. Advanced Engineering Sciences, 54(3): 25-35 (in Chinese with English abstract).

Zhou, X. Z., Wen, H. J., Li, Z. W., et al., 2022. An Interpretable Model for the Susceptibility of Rainfall-Induced Shallow Landslides Based on SHAP and XGBoost. Geocarto International, 37(26): 13419-13450. https://doi.org/10.1080/10106049.2022.2076928

Zhu, C. Q., 2014. Landslide Stability and Contol Analysis of Huanglianshu in Fengjie County (Dissertation). Chongqing University, Chongqing (in Chinese with English abstract).

郭子正, 殷坤龙, 黄发明, 等, 2019. 基于滑坡分类和加权频率比模型的滑坡易发性评价. 岩石力学与工程学报, 38(2): 287-300. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201902007.htm

黄发明, 曹昱, 范宣梅, 等, 2021. 不同滑坡边界及其空间形状对滑坡易发性预测不确定性的影响规律. 岩石力学与工程学报, 40(S2): 3227-3240. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2021S2023.htm

黄发明, 陈佳武, 范宣梅, 等, 2022. 降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模. 地球科学, 47(12): 4609-4628. doi: 10.3799/dqkx.2021.164

黄发明, 汪洋, 董志良, 等, 2019. 基于灰色关联度模型的区域滑坡敏感性评价. 地球科学, 44(2): 664-676. doi: 10.3799/dqkx.2018.175

李文彬, 范宣梅, 黄发明, 等, 2021. 不同环境因子联接和预测模型的滑坡易发性建模不确定性. 地球科学, 46(10): 3777-3795. doi: 10.3799/dqkx.2021.042

吴润泽, 胡旭东, 梅红波, 等, 2021. 基于随机森林的滑坡空间易发性评价: 以三峡库区湖北段为例. 地球科学, 46(1): 321-330. doi: 10.3799/dqkx.2020.032

肖婷, 2020. 三峡库区万州区及重点库岸段滑坡灾害风险评价(博士学位论文). 武汉: 中国地质大学.

叶润青, 李士垚, 郭飞, 等, 2021. 基于RS和GIS的三峡库区滑坡易发程度与土地利用变化的关系研究. 工程地质学报, 29(3): 724-733. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202103015.htm

殷跃平, 王鲁琦, 赵鹏, 等, 2022. 三峡库区高陡岸坡溃屈失稳机理及防治研究. 水利学报, 53(4): 379-391. https://www.cnki.com.cn/Article/CJFDTOTAL-SLXB202204001.htm

周晓亭, 黄发明, 吴伟成, 等, 2022. 基于耦合信息量法选择负样本的区域滑坡易发性预测. 工程科学与技术, 54(3): 25-35. https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH202203003.htm

朱灿群, 2014. 奉节县黄莲树滑坡稳定性及治理分析(硕士学位论文). 重庆: 重庆大学.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(11) / Tables(4)

Get Citation

PDF

XML

Article views (1353) PDF downloads(106)

Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing

doi: 10.3799/dqkx.2022.309

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content