基于AI技术的滑坡易发性制图研究进展

刘聪; 陈永吉; 张条; 卢全中

doi:10.3799/dqkx.2024.114

Volume 50 Issue 6

Jun. 2025

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Article Navigation > Earth Science > 2025 > 50(6): 2270-2283

Liu Cong, Chen Yongji, Zhang Tiao, Lu Quanzhong, 2025. Landslide Susceptibility Mapping Based on AI Technology. Earth Science, 50(6): 2270-2283. doi: 10.3799/dqkx.2024.114

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Liu Cong, Chen Yongji, Zhang Tiao, Lu Quanzhong, 2025. Landslide Susceptibility Mapping Based on AI Technology. Earth Science, 50(6): 2270-2283. doi: 10.3799/dqkx.2024.114

Liu Cong, Chen Yongji, Zhang Tiao, Lu Quanzhong, 2025. Landslide Susceptibility Mapping Based on AI Technology. Earth Science, 50(6): 2270-2283. doi: 10.3799/dqkx.2024.114

Citation:

Liu Cong, Chen Yongji, Zhang Tiao, Lu Quanzhong, 2025. Landslide Susceptibility Mapping Based on AI Technology. Earth Science, 50(6): 2270-2283. doi: 10.3799/dqkx.2024.114

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Landslide Susceptibility Mapping Based on AI Technology

doi: 10.3799/dqkx.2024.114

Liu Cong^{1, 2, 3
,
,},
Chen Yongji¹,
Zhang Tiao¹,
Lu Quanzhong^{1, 2, 3
,
,}

1.
School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
2.
Key Laboratory of Earth Fissures Geological Disaster, Ministry of Natural Resources, Nanjing 210018, China
3.
Field Scientific Observation and Research Station of Ground Fissure and Land Subsidence in Xi'an of Shaanxi, Ministry of Natural Resources, Xi'an 710054, China

Received Date: 2024-04-26
Available Online: 2025-07-11
Publish Date: 2025-06-25

Abstract

Abstract

Utilizing AI technology for landslide susceptibility mapping offers the advantages of efficiency and accuracy.To promote its application in landslide disaster prevention and control, in this article it introduces and summarizes the principles and characteristics of machine learning, deep learning, and ensemble learning models. Representative models such as Support Vector Machines, Deep Random Forests, and Random Forests were applied for analysis in Lueyang County, Shaanxi Province. It discusses the application and development directions of AI technology in the field of landslide susceptibility. The results indicate that ensemble learning models based on decision trees, compared to logistic regression and support vector machines, demonstrate higher efficacy with AUC values above 0.90. Under the commonly used class imbalance sampling strategy in LSM (landslide susceptibility mapping), ensemble models based on Boosting show advantages and are relatively less affected by sampling ratios. Generative Adversarial Networks can enhance the performance of deep learning models under data constraints, where in this study, the AUC value increased from 0.77 to 0.82. Combining landslide theoretical models with AI data models has great potential; leveraging AI models that fully utilize time-series data can improve model performance and help reveal the chain disaster effects and spatio-temporal evolution characteristics of landslides; conducting systematic studies of various learning models is of significant importance for the application of AI technology in landslide susceptibility mapping.
- landslide susceptibility mapping,
- deep learning,
- machine learning,
- ensemble learning,
- engineering geology

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References(47)

References

Alkhasawneh, M. S., Ngah, U. K., Tay, L. T., et al., 2014. Modeling and Testing Landslide Hazard Using Decision Tree. Journal of Applied Mathematics, (1): 929768. https://doi.org/10.1155/2014/929768

Breiman, L, 1996. Bagging Predictors. Machine Learning, 24: 123-140.

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

Catani, F., Lagomarsino, D., Segoni, S., et al., 2013. Landslide Susceptibility Estimation by Random Forests Technique: Sensitivity and Scaling Issues. Natural Hazards and Earth System Sciences, 13(11): 2815-2831. https://doi.org/10.5194/nhess-13-2815-2013

Chang, Z. L., Catani, F., Huang, F. M., et al., 2023. Landslide Susceptibility Prediction Using Sope Unit-Based Machine Learning Models Considering the Heterogeneity of Conditioning Factors. Journal of Rock Mechanics and Geotechnical Engineering, 15(5): 1127-1143. https://doi.org/10.1016/j.jrmge.2022.07.009

Dikshit, A., Pradhan, B., Alamri, A. M., 2021. Pathways and Challenges of the Application of Artificial Intelligence to Geohazards Modelling. Gondwana Research, 100: 290-301. https://doi.org/10.1016/j.gr.2020.08.007

Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al., 2020. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. ArXiv e-Prints: arXiv: 2010.11929. https://doi.org/10.48550/arXiv.2010.11929

Dou, J., Xiang, Z. L., Xu, Q., et al., 2023. Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation. Earth Science, 48(5): 1657-1674(in Chinese with English abstract).

Fang, Z. C., Wang, Y., Peng, L., et al., 2020. Integration of Convolutional Neural Network and Conventional Machine Learning Classifiers for Landslide Susceptibility Mapping. Computers & Geosciences, 139: 104470. https://doi.org/10.1016/j.cageo.2020.104470

Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8): 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Huang, Y., Zhao, L., 2018. Review on Landslide Susceptibility Mapping Using Support Vector Machines. Catena, 165: 520-529. https://doi.org/10.1016/j.catena.2018.03.003

Jia, J., Mao, Y. M., Meng, X. J., et al., 2023. Comparison of Landslide Susceptibility Evaluation by Deep Random Forest and Random Forest Model: A Case Study of Lueyang County, Hanzhong City. Northwestern Geology, 56(3): 239-249(in Chinese with English abstract).

Kirschbaum, D., Stanley, T., 2018. Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness. Earth's Future, 6(3): 505-523. https://doi.org/10.1002/2017ef000715

Krizhevsky, A., Sutskever, I., Hinton, G., 2012. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6): 84-90. https://doi.org/10.1145/3065386

Li, L. P., Lan, H. X., Guo, C. B., et al., 2017. Geohazard Susceptibility Assessment along the Sichuan-Tibet Railway and Its Adjacent Area Using an Improved Frequency Ratio Method. Geoscience, 31(5): 911-929 (in Chinese with English abstract).

Li, X. P., Chong, J. X., Lu, Y. B., et al., 2022. Application of Information Gain in the Selection of Factors for Regional Slope Stability Evaluation. Bulletin of Engineering Geology and the Environment, 81(11): 470. https://doi.org/10.1007/s10064-022-02970-y

Liu, F., Deng, Y. H., Mu, H. D., et al., 2023. A Study of the Stability Evaluation Method of Rainfall-Induced Shallow Loess Landslides Based on the Maxent-Sinmap Slope Model. Hydrogeology & Engineering Geology, 50(5): 146-158(in Chinese with English abstract).

Liu, S. L., Wang, L. Q., Zhang, W. G., et al., 2023. A Comprehensive Review of Machine Learning-Based Methods in Landslide Susceptibility Mapping. Geological Journal, 58(6): 2283-2301. https://doi.org/10.1002/gj.4666

Lyu, L., Chen, T., Li, J., 2023. An Interpretation Study on the ML Models for Landslide Susceptibility Mapping. IGARSS 2023—2023 IEEE International Geoscience and Remote Sensing Symposium. July 16-21, 2023, Pasadena, CA, USA. IEEE, 7241-7244. https://doi.org/10.1109/igarss52108.2023.10282171

Miao, F. S., Wu, Y. P., Xie, Y. H., et al., 2018. Prediction of Landslide Displacement with Step-Like Behavior Based on Multialgorithm Optimization and a Support Vector Regression Model. Landslides, 15(3): 475-488. https://doi.org/10.1007/s10346-017-0883-y

Miao, F. S., Zhao, F. C., Wu, Y. P., et al., 2023. Landslide Susceptibility Mapping in Three Gorges Reservoir Area Based on GIS and Boosting Decision Tree Model. Stochastic Environmental Research and Risk Assessment, 37(6): 2283-2303. https://doi.org/10.1007/s00477-023-02394-4

Ngo, P. T. T., Panahi, M., Khosravi, K., et al., 2021. Evaluation of Deep Learning Algorithms for National Scale Landslide Susceptibility Mapping of Iran. Geoscience Frontiers, 12(2): 505-519. https://doi.org/10.1016/j.gsf.2020.06.013

Pham, B. T., Prakash, I., Singh, S. K., et al., 2019. Landslide Susceptibility Modeling Using Reduced Error Pruning Trees and Different Ensemble Techniques: Hybrid Machine Learning Approaches. Catena, 175: 203-218. https://doi.org/10.1016/j.catena.2018.12.018

Pradhan, B., Dikshit, A., Lee, S., et al., 2023. An Explainable AI (XAI) Model for Landslide Susceptibility Modeling. Applied Soft Computing, 142. https://doi.org/10.1016/j.asoc.2023.110324

Reichenbach, P., Rossi, M., Malamud, B. D., et al., 2018. A Review of Statistically-Based Landslide Susceptibility Models. Earth-Science Reviews, 180: 60-91. https://doi.org/10.1016/j.earscirev.2018.03.001

Sahin, E. K., 2022. Comparative Analysis of Gradient Boosting Algorithms for Landslide Susceptibility Mapping. Geocarto International, 37(9): 2441-2465. https://doi.org/10.1080/10106049.2020.1831623

Saito, H., Nakayama, D., Matsuyama, H., 2009. Comparison of Landslide Susceptibility Based on a Decision-Tree Model and Actual Landslide Occurrence: The Akaishi Mountains, Japan. Geomorphology, 109(3-4): 108-121. https://doi.org/10.1016/j.geomorph.2009.02.026.

Stott, P., 2016. How Climate Change Affects Extreme Weather Events. Science, 352(6293): 1517-1518. https://doi.org/10.1126/science.aaf7271

Taalab, K., Cheng, T., Zhang, Y., 2018. Mapping Landslide Susceptibility and Types Using Random Forest. Big Earth Data, 2(2): 159-178. https://doi.org/10.1080/20964471.2018.1472392

Tian, N. M., Lan, H. X., Wu, Y. M., et al., 2020. Performance Comparison of BP Artificial Neural Network and CART Decision Tree Model in Landslide Susceptibility Prediction. Journal of Geo-Information Science, 22(12): 2304-2316(in Chinese with English abstract).

van Westen, C. J., van Asch, T. W. J., Soeters, R., 2006. Landslide Hazard and Risk Zonation: Why is It still so Difficult?Bulletin of Engineering Geology and the Environment, 65(2): 167-184. https://doi.org/10.1007/s10064-005-0023-0

Wang, H. J., Wang, L., Zhang, L. M., 2023. Transfer Learning Improves Landslide Susceptibility Assessment. Gondwana Research, 123: 238-254. https://doi.org/10.1016/j.gr.2022.07.008

Wang, H. J., Zhang L., Luo H., et al., 2021. AI-Powered Landslide Susceptibility Assessment in Hong Kong. Engineering Geology, 288: 106103. https://doi.org/10.1016/j.enggeo.2021.106103

Wang, Y., Fang, Z. C., Hong, H. Y., 2019. Comparison of Convolutional Neural Networks for Landslide Susceptibility Mapping in Yanshan County, China. Science of the Total Environment, 666: 975-993. https://doi.org/10.1016/j.scitotenv.2019.02.263

Wang, Y. K., Tang, H. M., Huang, J. S., et al., 2022. A Comparative Study of Different Machine Learning Methods for Reservoir Landslide Displacement Prediction. Engineering Geology, 298: 106544. https://doi.org/10.1016/j.enggeo.2022.106544

Ward, P. J., Blauhut, V., Bloemendaal, N., et al., 2020. Review Article: Natural Hazard Risk Assessments at the Global Scale. Natural Hazards and Earth System Sciences, 20(4): 1069-1096. https://doi.org/10.5194/nhess-20-1069-2020

Xu, S. H., Liu, J. P., Wang, X. H., et al., 2020. Landslide Susceptibility Assessment Method Incorporating Index of Entropy Based on Support Vector Machine: A Case Study of Shaanxi Province. Geomatics and Information Science of Wuhan University, 45(8): 1214-1222(in Chinese with English abstract).

Yang, C., Liu, L. L., Huang, F. M., et al., 2023. Machine Learning-Based Landslide Susceptibility Assessment with Optimized Ratio of Landslide to Non-Landslide Samples. Gondwana Research, 123: 198-216. https://doi.org/10.1016/j.gr.2022.05.012

Zhang, W. G., He, Y. W., Wang, L. Q., et al., 2023. Landslide Susceptibility Mapping Using Random Forest and Extreme Gradient Boosting: A Case Study of Fengjie, Chongqing. Geological Journal, 58(6): 2372-2387. https://doi.org/10.1002/gj.4683

Zhu, J. J., Yang, M. Q., Ren, Z. J., 2023. Machine Learning in Environmental Research: Common Pitfalls and Best Practices. Environmental Science & Technology, 57(46): 17671-17689. https://doi.org/10.1021/acs.est.3c00026

Zuccaro, G., De Gregorio, D., Leone, M. F., 2018. Theoretical Model for Cascading Effects Analyses. International Journal of Disaster Risk Reduction, 30: 199-215. https://doi.org/10.1016/j.ijdrr.2018.04.019

窦杰, 向子林, 许强, 等, 2023. 机器学习在滑坡智能防灾减灾中的应用与发展趋势. 地球科学, 48(5): 1657-1674. doi: 10.3799/dqkx.2022.419

贾俊, 毛伊敏, 孟晓捷, 等, 2023. 深度随机森林和随机森林算法的滑坡易发性评价对比: 以汉中市略阳县为例. 西北地质, 56(3): 239-249.

李郎平, 兰恒星, 郭长宝, 等, 2017. 基于改进频率比法的川藏铁路沿线及邻区地质灾害易发性分区评价. 现代地质, 31(5): 911-929.

刘凡, 邓亚虹, 慕焕东, 等, 2023. 基于最大熵-无限边坡模型的降雨诱发浅层黄土滑坡稳定性评价方法研究. 水文地质工程地质, 50(5): 146-158.

田乃满, 兰恒星, 伍宇明, 等, 2020. 人工神经网络和决策树模型在滑坡易发性分析中的性能对比. 地球信息科学学报, 22(12): 2304-2316.

徐胜华, 刘纪平, 王想红, 等, 2020. 熵指数融入支持向量机的滑坡灾害易发性评价方法: 以陕西省为例. 武汉大学学报(信息科学版), 45(8): 1214-1222.

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Landslide Susceptibility Mapping Based on AI Technology

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