基于PU-bagging负样本采样的白龙江流域泥石流易发性分级评价

doi:10.3799/dqkx.2025.117

基于PU-bagging负样本采样的白龙江流域泥石流易发性分级评价

doi: 10.3799/dqkx.2025.117

1 中国地质大学工程学院, 湖北武汉 430074;
2 中国地质调查局水文地质环境地质调查中心, 天津 300309;
3 同济大学土木工程学院, 上海 200092;
4 天津水环九恒科技有限公司, 天津 300304

基金项目:

国家自然科学基金项目-面上项目（42477150）

详细信息

作者简介:
万芹江(2002-),女,硕士研究生,研究方向为地质灾害易发性评价.E-mail:wanqinjiang@cug.edu.cn ORCID:0009-0009-4196-2875.

通讯作者:
王洪磊(1982-),男,正高级工程师,主要工作为地质灾害调查评价与监测预警.E-mail:270987133@qq.com

中图分类号: P642.23
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- 文章访问数: 26
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出版历程
- 收稿日期: 2025-05-06
- 网络出版日期: 2025-07-01

Classification assessment of debris flow susceptibility in Bailong River Basin based on PU-bagging negative sampling

1 Faculty of Engineering, China University of Geosciences, Wuhan 430074, China;
2 Center for Hydrogeology and Environmental Geology of CGS, Tianjin 300309, China;
3 College of Civil Engineering, Tongji University, Shanghai 200092, China;
4 Tianjin Shuihuan Jiuheng Technology Co., LTD., Tianjin 300304, China

摘要

摘要: 为提高白龙江流域泥石流易发分区的可靠性，建立随机森林为基学习器的PU-bagging负样本采样模型。选取高程、降水量等评价因子，使用逻辑回归、随机森林、支持向量机和XGBoost算法，构建白龙江流域泥石流易发性评价模型。根据混淆矩阵衍生的评价指标、ROC曲线和五种分级方法，对比分析了四种模型的性能，并利用SHAP分析评价因子对模型的贡献程度。结果表明：（1）支持向量机模型结合几何间隔分级方法的灾害识别精度提升了24%。（2）随机森林模型能够识别更多的潜在泥石流样本，而XGBoost模型可减少对非灾害样本的误判。（3） SHAP值对高程变化的敏感性间接反映了高差对泥石流发育的重要性。本研究可以为白龙江流域新型城镇化建设与泥石流防治工程的规划提供数据支撑。
- 白龙江流域 /
- 样本采样 /
- 易发性 /
- 泥石流 /
- 分级方法
Abstract: To improve the reliability of the debris flow-prone zones in the Bailong River Basin, a PU-bagging negative sample sampling model based on the random forest as the base learner is established. Evaluation factors such as elevation and precipitation were selected, and logistic regression, random forest, support vector machine and XGBoost algorithms were used to construct an evaluation model for the susceptibility of debris flows in the Bailong River Basin. Based on the evaluation indicators derived from the confusion matrix, the ROC curve and five classification methods, the performances of the four models were compared and analyzed, and the contribution degree of the evaluation factors to the model was analyzed by using SHAP. The results show that: (1) The disaster identification accuracy of the support vector machine model combined with the geometric interval classification method has increased by 24%. (2) The random forest model can identify more potential debris flow samples, while the XGBoost model can reduce the misjudgment of non-disaster samples. (3) The sensitivity of SHAP values to elevation changes indirectly reflects the importance of height differences for the development of debris flows. This research can provide data support for the planning of the new urbanization construction and debris flow prevention and control project in the Bailong River Basin.
- Bailong River Basin /
- sample sampling /
- susceptibility /
- debris flow /
- classification method

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基于PU-bagging负样本采样的白龙江流域泥石流易发性分级评价

doi: 10.3799/dqkx.2025.117

作者简介:
万芹江(2002-),女,硕士研究生,研究方向为地质灾害易发性评价.E-mail:wanqinjiang@cug.edu.cn ORCID:0009-0009-4196-2875.

通讯作者:
王洪磊(1982-),男,正高级工程师,主要工作为地质灾害调查评价与监测预警.E-mail:270987133@qq.com

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Classification assessment of debris flow susceptibility in Bailong River Basin based on PU-bagging negative sampling

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留言板

基于PU-bagging负样本采样的白龙江流域泥石流易发性分级评价

doi: 10.3799/dqkx.2025.117

作者简介: 万芹江(2002-),女,硕士研究生,研究方向为地质灾害易发性评价.E-mail:wanqinjiang@cug.edu.cn ORCID:0009-0009-4196-2875.

通讯作者: 王洪磊(1982-),男,正高级工程师,主要工作为地质灾害调查评价与监测预警.E-mail:270987133@qq.com

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出版历程

Classification assessment of debris flow susceptibility in Bailong River Basin based on PU-bagging negative sampling

计量

出版历程

目录

作者简介:
万芹江(2002-),女,硕士研究生,研究方向为地质灾害易发性评价.E-mail:wanqinjiang@cug.edu.cn ORCID:0009-0009-4196-2875.

通讯作者:
王洪磊(1982-),男,正高级工程师,主要工作为地质灾害调查评价与监测预警.E-mail:270987133@qq.com