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基于CatBoost-SHAP模型的滑坡易发性建模及可解释性

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曾韬睿, 王林峰, 张俞, 程平, 吴帆. 基于CatBoost-SHAP模型的滑坡易发性建模及可解释性[J]. 中国地质灾害与防治学报, 2024, 35(1): 37-50. doi: 10.16031/j.cnki.issn.1003-8035.202309035
引用本文: 曾韬睿, 王林峰, 张俞, 程平, 吴帆. 基于CatBoost-SHAP模型的滑坡易发性建模及可解释性[J]. 中国地质灾害与防治学报, 2024, 35(1): 37-50. doi: 10.16031/j.cnki.issn.1003-8035.202309035
shu
ZENG Taorui, WANG Linfeng, ZHANG Yu, CHENG Ping, WU Fan. Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(1): 37-50. doi: 10.16031/j.cnki.issn.1003-8035.202309035
Citation: ZENG Taorui, WANG Linfeng, ZHANG Yu, CHENG Ping, WU Fan. Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(1): 37-50. doi: 10.16031/j.cnki.issn.1003-8035.202309035
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基于CatBoost-SHAP模型的滑坡易发性建模及可解释性

  • 1.

    重庆交通大学山区公路水运交通地质减灾重庆市高校市级重点实验室，重庆　400074

  • 2.

    重庆交通大学河海学院，重庆　400074

  • 3.

    浙江省地矿科技有限公司，浙江 杭州　310007

  • 4.

    温州工程勘察院有限公司，浙江 温州　325006

  • 5.

    山地灾害与地表过程重点实验室/中国科学院山地灾害与环境研究所，四川 成都　610299

  • 基金项目: 国家自然科学基金联合基金项目（U22A20600）；重庆市研究生导师团队建设项目（JDDSTD2022009）；重庆人才计划创新与创业示范团队（CQYc-201903204）；国家自然科学基金项目（51708068）
详细信息
    作者简介: 曾韬睿（1995—），男，重庆，博士研究生，主要从事滑坡灾害风险评价与管理研究。E-mail：zengtaorui@cug.edu.cn
    通讯作者: 王林峰（1983—），男，教授，博士，研究方向为地质灾害减灾理论与技术。E-mail：wanglinfeng@cqjtu.edu.cn

  • 中图分类号: P642.22

Landslide susceptibility modeling and interpretability based on CatBoost-SHAP model

  • 1.

    Key Laboratories of Mountainous Area Highway Transportation and Transportation Geological Disaster Reduction in University of Chongqing, Chongqing Jiaotong University, Chongqing　400074, China

  • 2.

    School of Hehai, Chongqing Jiaotong University, Chongqing　400074, China

  • 3.

    Zhejiang Geology and Mineral Technology Co. Ltd., Hangzhou, Zhejiang　310007, China

  • 4.

    Wenzhou Engineering Survey Institute Co. Ltd., Wenzhou, Zhejiang　325006, China

  • 5.

    Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan　610299, China

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    摘要

  • 文章致力于深入探索滑坡易发性建模中集成学习模型的不确定性和可解释性。以浙江省东部沿海山区为研究对象，利用谷歌历史影像与Sentinel-2A影像，记录了2016年超级台风“鲇鱼”触发的552起浅层滑坡事件。研究首先对连续型因子进行了不分级、等间距法和自然断点法的工况设计，进一步划分为4，6，8，12，16，20级。随后，引入了类别增强提升树模型（CatBoost）以评估不同工况下的滑坡易发性值，再结合受试者曲线与沙普利加性解释法分析，对建模过程中的不确定性和可解释性进行了深入研究，目的在于确定最优建模策略。结果表明：（1） 在CatBoost模型计算中，河流距离成为最关键的影响因子，其次是与地质条件、人类活动相关的因子；（2） 不分级工况下，模型能够获得最高的AUC值，达到0.866；（3）相较于等间距法，自然断点法的划分策略展现出更佳的泛化能力，且模型预测性能随着分级数量的增加而增加；（4）沙普利加性解释法模型揭示了主要影响因子道路距离、河流距离、DEM和坡向对台风诱发滑坡的控制机制。研究成果能够加深对滑坡易发性的理解，提高滑坡预测的准确性和可靠性，为相关地区的防灾减灾工作提供科学依据。

    This study is dedicated to delving deeply into the uncertainty and interpretability of ensemble learning models in landslide susceptibility modeling. Focusing on the eastern coastal mountainous region of Zhejiang Province as the study area, this research utilizes historical Google imagery and Sentinel-2A imagery to document 552 shallow landslide events triggered by the super typhoon "Megi" in 2016. Initially, the study designs scenarios for continuous factors using non-grading, equal interval method, and natural breaks method, subsequently subdividing them into 4, 6, 8, 12, 16, 20 levels. Thereafter, the Category Boosting Model (CatBoost) is introduced to assess landslide susceptibility values under different scenarios. Coupled with the analysis of ROC (receiver operating characteristic) curves and SHAP (SHapley Additive exPlanation), in-depth investigation into uncertainty and interpretability during the modeling process is conducted, with the aim of determining the optimal modeling strategy. The results indicate that: (1) In the computations of the CatBoost model, aspect emerges as the most critical influencing factor, followed by factors related to water and geological conditions; (2) Under the non-grading scenario, the model achieves the highest AUC value, reaching 0.866; (3) Compared to the equal interval method, the natural breaks method demonstrates superior generalization capability, and the model’s predictive performance imrpoves with an increase in the number of classifications; (4) The SHAP model reveals the controlling mechanisms of the principal influencing factors (aspect, lithology, elevation, and road distance) on typhoon-induced landslides. The findings of this research can deepen our understanding of landslide susceptibility, enhance the accuracy and reliability of landslide predictions, and provide a scientific basis for disaster prevention and mitigation efforts in the related regions.

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  • 图 1  研究区概况图

    Figure 1. 

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    图 2  影响因子

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    图 3  不同分级策略的滑坡易发性建模及可解释性流程图

    Figure 3. 

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    图 4  因子共线性及平均重要性分析

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    图 5  因子在不同数据集中的重要性，EI-Equal Interval等间距，NB-Natural Breaks自然断点

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    图 6  不同分级条件下十折交叉验证的AUC值

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    图 7  不同数据集ROC曲线

    Figure 7. 

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    图 8  影响因子蜂群图

    Figure 8. 

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    图 9  重要影响因子散点图

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    图 10  研究区滑坡易发性图

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收稿日期:  2023-09-26
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