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| Citation: | Luo Jing, Song Chen, Pei Xiangjun, Yan Rui, Liu Ziqiang, Li Guobing, Wu Jingang, Liu Hongyang, 2026. Rainfall-Induced Landslide Susceptibility Assessment Based on Collaborative Sample Optimization Using Multi-Source Remote Sensing Interpretation and Physical Model Simulation. Earth Science, 51(9): 3469-3483. doi: 10.3799/dqkx.2026.271
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Against the background of global climate change, increasingly frequent extreme rainfall events have intensified the risk of rainfall-induced landslides in mountainous regions, highlighting the importance of accurate landslide susceptibility assessment. The quality of training samples, particularly negative sample selection, is a key factor affecting the reliability of machine learning-based susceptibility models. This study focuses on a section of the Laibao Expressway severely affected by the Beijing "23·7" extreme rainfall event. DInSAR, NDVI, and optical remote sensing imagery were integrated to identify geological hazards. The coupled TRIGRS-Scoops3D model was then used to evaluate slope stability under rainfall infiltration conditions, and the results were used to optimize negative sample selection. Logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost models were further developed for landslide susceptibility assessment. Their predictive performance was evaluated using receiver operating characteristic (ROC) curves and related performance metrics, while SHAP analysis was used to quantify the contributions of conditioning factors. The results show that the Beijing "23·7" extreme rainfall event triggered 466 geological hazards, including 345 landslides, mainly distributed on mountainous slopes with pronounced topographic relief and strong valley incision. The TRIGRS-Scoops3D model showed capability in identifying potentially unstable areas, with 90.43% of the observed landslides falling within unstable zones and a %LRclass value of 80.09%, representing improvements of 15.36% and 9.43%, respectively, over the TRIGRS model. After negative sample selection was optimized using the physics-based model, XGBoost achieved the best performance, with an AUC of 0.956, followed by SVM and RF with AUC values of 0.906 and 0.899, respectively, while LR yielded a relatively lower AUC of 0.861. SHAP analysis further indicated that slope gradient and rainfall were the dominant factors controlling rainfall-induced landslide susceptibility in the study area. The findings provide a useful reference for landslide susceptibility assessment in complex mountainous regions and for geological hazard prevention and mitigation along mountainous transportation corridors.
|
Alvioli, M., Spiga, D., Baum, L. R., 2016. Evaluation of the Parallel Performance of the TRIGRS v2.1 Model for Rainfall-Induced Landslides. PeerJ PrePrints, 4e2206.
|
|
Bai, S. B., Lü, G. N., Sheng, Y. H., et al., 2005. Analysis of Landslide Causative Factors Using GIS in the Three Gorges Reservoir Area, China. Journal of Mountain Research, 23(1): 63-70 (in Chinese with English abstract).
|
|
Berardino, P., Fornaro, G., Lanari, R., et al., 2002. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383. https://doi.org/10.1109/TGRS.2002.803792
|
|
Breiman, L., 2001. Random Forests. Machine Learning, 45(1): 5-32. https://doi.org/10.1023/A:1010933404324
|
|
Bui, D. T., Tsangaratos, P., Nguyen, V. T., et al., 2020. Comparing the Prediction Performance of a Deep Learning Neural Network Model with Conventional Machine Learning Models in Landslide Susceptibility Assessment. CATENA, 188: 104426. https://doi.org/10.1016/j.catena.2019.104426
|
|
Chang, J. Y., Bao, H., Wu, F. Q., et al., 2015. Discussion on Stability of Shallow Landslide under Rainfall. Rock and Soil Mechanics, 36(4): 995-1001 (in Chinese with English abstract).
|
|
Chang, M., Xu, Q., Wang, Y. S., et al., 2024. Development Characteristics and Disaster-Causing Mechanisms of the "8·3" Catastrophic Flash Flood and Debris Flow in Guzan, Kangding, Sichuan Province. Geomatics and Information Science of Wuhan University, 49(11): 2136-2144 (in Chinese with English abstract).
|
|
Chen, T. Q., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco.
|
|
Cortes, C., Vapnik, V., 1995. Support-Vector Networks. Machine Learning, 20(3): 273-297. https://doi.org/10.1007/BF00994018
|
|
Dong, X. J., Xu, Q., She, J. X., et al., 2020. Preliminary Study on Interpretation of Geological Hazards in Jiuzhaigou Based on Multi-Source Remote Sensing Data. Geomatics and Information Science of Wuhan University, 45(3): 432-441 (in Chinese with English abstract).
|
|
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., et al., 2019. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing, 11(2): 196. https://doi.org/10.3390/rs11020196
|
|
He, J. Y., 2021. Stability Analysis of Rainfall-Induced Shallow Landslide: Based on TRIGRS Model and Scoops3D Model (Dissertation). Northwest University, Xi'an (in Chinese with English abstract).
|
|
Hosmer, D. W., Lemeshow, S., Sturdivant, R. X., 2013. Applied Logistic Regression (3rd Edition). Wiley, Hoboken. https://doi.org/10.1002/9781118548387
|
|
Huang, F. M., Xiong, H. W., Jiang, S. H., et al., 2024. Modelling Landslide Susceptibility Prediction: A Review and Construction of Semi-Supervised Imbalanced Theory. Earth-Science Reviews, 250: 104700. https://doi.org/10.1016/j.earscirev.2024.104700
|
|
Huang, R. Q., Qi, G. Q., 2002. The Effect of Unsaturated Soil Suction on Slope Stability. Journal of Engineering Geology, 10(4): 343-348 (in Chinese with English abstract).
|
|
Huang, R. Q., Xu, Q., Qi, G. Q., 2007. Evaluation and Prediction of Rainfall and Reservoir-Induced Landslides. Science Press, Beijing (in Chinese).
|
|
Jia, Z., Cheng, Z. J., Chang, Z. L., et al., 2026. Non-Landslide Sample for Landslide Susceptibility Prediction Modeling: A Review of Selection Strategies and Their Influence Rules. Journal of Rock Mechanics and Geotechnical Engineering, 18(4): 2859-2880. https://doi.org/10.1016/j.jrmge.2025.12.011
|
|
Li, H. Q., Sun, H. Y., Sun, X. M., et al., 2009. Influence of Rainfall Infiltration on Slopes by Physical Model Test. Chinese Journal of Geotechnical Engineering, 31(4): 589-594 (in Chinese with English abstract).
|
|
Liao, M. S., 2014. Principles and Applications of InSAR. Beijing: Science Press (in Chinese).
|
|
Liu, L. L., Duan, C., Xu, C., et al., 2026. A Physically-Informed Negative Sampling Strategy for Earthquake-Induced Landslide Susceptibility Assessment. Earth Science, Online (in Chinese with English abstract).
|
|
Liu, S. W., Guo, D. H., Chen, W. T., et al., 2012. The Application of Airborne Lidar Technology in Landslide Investigation and Monitoring of Three Gorges Reservoir Area. Geology in China, 39(2): 507-517 (in Chinese with English abstract).
|
|
Ma, H. C., 2011. Review on Applications of LiDAR Mapping Technology to Geosciences. Earth Science, 36(2): 347-354 (in Chinese with English abstract).
|
|
Merghadi, A., Yunus, A. P., Dou, J., et al., 2020. Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance. Earth-Science Reviews, 207: 103225. https://doi.org/10.1016/j.earscirev.2020.103225
|
|
Muntohar, A. S., Liao, H. J., 2010. Rainfall Infiltration: Infinite Slope Model for Landslides Triggering by Rainstorm. Natural Hazards, 54(3): 967-984. https://doi.org/10.1007/s11069-010-9518-5
|
|
Nan, Y., Zhai, S. H., Li, Y., et al., 2024. Analysis on the Characteristics of Geological Disasters and Effectiveness of Early Warning Duiring Heavy Rainfall on "23·7" in Beijing. The Chinese Journal of Geological Hazard and Control, 35(2): 66-73 (in Chinese with English abstract).
|
|
Nichol, J. E., Shaker, A., Wong, M. S., 2006. Application of High-Resolution Stereo Satellite Images to Detailed Landslide Hazard Assessment. Geomorphology, 76(1-2): 68-75. https://doi.org/10.1016/j.geomorph.2005.10.001
|
|
Qian, J. Y., Zhang, G., Zhang, J. M., et al., 2009. Centrifuge Model Tests of Cohesive Soil Slopes during Rainfall. Journal of Tsinghua University (Science and Technology), 49(6): 829-833 (in Chinese with English abstract).
|
|
Tian, Y., Gao, B., Yin, H., et al., 2024. Handling Imbalanced Samples in Landslide Susceptibility Evaluation. Hydrogeology & Engineering Geology, 51(6): 171-181 (in Chinese with English abstract).
|
|
Tralli, D. M., Blom, R. G., Zlotnicki, V., et al., 2005. Satellite Remote Sensing of Earthquake, Volcano, Flood, Landslide and Coastal Inundation Hazards. ISPRS Journal of Photogrammetry and Remote Sensing, 59(4): 185-198. https://doi.org/10.1016/j.isprsjprs.2005.02.002
|
|
Wu, B., Shi, Z. M., Zheng, H. C., et al., 2024. Impact of Sampling for Landslide Susceptibility Assessment Using Interpretable Machine Learning Models. Bulletin of Engineering Geology and the Environment, 83(11): 461. https://doi.org/10.1007/s10064-024-03980-8
|
|
Xin, X., Zhang, F. Y., 2018. Application of a 3D Deterministic Model for Predicting Shallow Loess Landslide Stability. Chinese Journal of Engineering, 40(4): 397-406 (in Chinese with English abstract).
|
|
Zhang, Q. Y., 2023. The Cause and Enlightenment of the "23.7" Extremely Rainstorm Disaster in Fangshan District of Beijing City. China Flood & Drought Management, 33(10): 43-47 (in Chinese with English abstract).
|
|
Zhang, S., Wang, F. W., 2019. Three-Dimensional Seismic Slope Stability Assessment with the Application of Scoops3D and GIS: A Case Study in Atsuma, Hokkaido. Geoenvironmental Disasters, 6(1): 9. https://doi.org/10.1186/s40677-019-0125-9
|
|
Zhao, C. Y., Lu, Z., 2018. Remote Sensing of Landslides—A Review. Remote Sensing, 10(2): 279. https://doi.org/10.3390/rs10020279
|
|
Zheng, L. J., Li, X. Z., Yao, J., et al., 2023. Dynamic Prediction of Spatiotemporal Stability of Potential Landslides Based on the TRIGRS and Scoops3D Coupling Model. Journal of Natural Disasters, 32(2): 199-209 (in Chinese with English abstract).
|
|
Zhou, X. T., 2022. Recognition and Dynamic Susceptibility Assessment of Landslides Based on Multi-Source Data (Dissertation). East China University of Technology, Nanchang (in Chinese with English abstract).
|
|
白世彪, 闾国年, 盛业华, 等, 2005. 基于GIS的长江三峡库区滑坡影响因子分析. 山地学报, 23(1): 63-70.
|
|
常金源, 包含, 伍法权, 等, 2015. 降雨条件下浅层滑坡稳定性探讨. 岩土力学, 36(4): 995-1001.
|
|
常鸣, 许强, 王运生, 等, 2024. 四川康定姑咱"8·3"特大山洪泥石流发育特征及孕灾成因研究. 武汉大学学报(信息科学版), 49(11): 2136-2144.
|
|
董秀军, 许强, 佘金星, 等, 2020. 九寨沟核心景区多源遥感数据地质灾害解译初探. 武汉大学学报(信息科学版), 45(3): 432-441.
|
|
何简吟, 2021. 降雨型浅层滑坡稳定性研究(硕士学位论文). 西安: 西北大学.
|
|
黄润秋, 戚国庆, 2002. 非饱和渗流基质吸力对边坡稳定性的影响. 工程地质学报, 10(4): 343-348.
|
|
黄润秋, 许强, 戚国庆, 2007. 降雨及水库诱发滑坡的评价与预测. 北京: 科学出版社.
|
|
李焕强, 孙红月, 孙新民, 等, 2009. 降雨入渗对边坡性状影响的模型实验研究. 岩土工程学报, 31(4): 589-594.
|
|
廖明生, 2014. InSAR原理与应用. 北京: 科学出版社.
|
|
刘磊磊, 段灿, 许冲, 等, 2026. 融合物理机制的负样本优化策略与地震滑坡易发性评价. 地球科学, 知网首发.
|
|
刘圣伟, 郭大海, 陈伟涛, 等, 2012. 机载激光雷达技术在长江三峡工程库区滑坡灾害调查和监测中的应用研究. 中国地质, 39(2): 507-517.
|
|
马洪超, 2011. 激光雷达测量技术在地学中的若干应用. 地球科学, 36(2): 347-354. doi: 10.3799/dgkx.2011.037
|
|
南赟, 翟淑花, 李岩, 等, 2024. 北京地区"23·7"特大暴雨型地质灾害特征及预警成效分析. 中国地质灾害与防治学报, 35(2): 66-73.
|
|
钱纪芸, 张嘎, 张建民, 等, 2009. 降雨时黏性土边坡的离心模型试验. 清华大学学报(自然科学版), 49(6): 829-833.
|
|
田尤, 高波, 殷红, 等, 2024. 滑坡易发性评价中样本不均衡问题处理研究. 水文地质工程地质, 51(6): 171-181.
|
|
辛星, 张帆宇, 2018. 三维确定性模型在浅层黄土滑坡稳定性预测中的应用. 工程科学学报, 40(4): 397-406.
|
|
张启义, 2023. 北京房山区"23·7"特大暴雨灾害的成因及启示. 中国防汛抗旱, 33(10): 43-47.
|
|
郑玲静, 李秀珍, 姚杰, 等, 2023. 基于TRIGRS与Scoops3D耦合模型的潜在滑坡稳定性时空动态预测. 自然灾害学报, 32(2): 199-209.
|
|
周晓亭, 2022. 基于多源数据的滑坡识别及其易发性动态评价(博士学位论文). 南昌: 东华理工大学.
|
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