融合集成机器学习与负样本采样策略的降雨群发滑坡易发性评价

廖伟杰; 冯文凯; 彭超; 李飞; 杨忠亮; 宁康超; 赵家琛; 易小宇

doi:10.19509/j.cnki.dzkq.tb202512009

融合集成机器学习与负样本采样策略的降雨群发滑坡易发性评价

doi: 10.19509/j.cnki.dzkq.tb202512009

中国煤炭地质总局广东煤炭地质局勘查院，广州 510440
成都理工大学地质灾害防治与地质环境保护全国重点实验室，成都 610059
茂名市地质与海洋监测站，广东茂名 525099

基金项目: 国家自然科学基金项目（U2005205）；中国煤炭地质总局广东煤炭地质局勘查院项目（CGHT-ZMJN-2025-0054）

详细信息

作者简介:
廖伟杰：E-mail：593606181@qq.com

通讯作者:
E-mail：fengwenkai@cdut.cn

中图分类号: TP18；P642.22
计量
- 文章访问数: 179
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-12-23
- 录用日期: 2026-03-23
- 修回日期: 2026-02-28
- 网络出版日期: 2026-03-30

Susceptibility assessment of rainfall-induced clustered landslides integrating ensemble machine learning and negative sample sampling strategies

Exploration Institute, Guangdong Bureau of Coal Geology, CNACG, Guangzhou 510440, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Maoming Geological and Marine Monitoring Station, Maoming Guangdong 525099, China

More Information

Author Bio:
E-mail：593606181@qq.com

Corresponding author: E-mail：fengwenkai@cdut.cn

摘要

摘要:
我国南方丘陵山地降雨群发滑坡具备短时爆发、多点并发、空间集聚的特点，滑坡点位和周边稳定斜坡环境条件高度相似，负样本采样方案是制约滑坡易发性评价精度的关键瓶颈。传统负样本采样容易选取环境差异悬殊的简单负样本，造成模型难以识别滑坡区内部斜坡稳定性差异，产生大量预测误判。以 2023 年 10 月广东信宜−罗定交界极端降雨诱发群发滑坡为研究对象，探究不同负样本采样策略与机器学习模型耦合对滑坡易发性评价的影响。基于灾前、灾后 Planet 遥感影像目视解译得到 3792 处滑坡正样本；从气象水文、地形地貌、地质土壤、植被覆盖、人类活动 5 个维度筛选评价因子，通过斯皮尔曼相关性分析剔除存在强共线性的高程因子；在正负样本 1∶1、统一设置 100 m 滑坡缓冲区的条件下构建缓冲区随机采样（非滑坡区采样）、低坡度约束采样（低坡度区采样）、K-means 聚类采样 3 类负样本集；采用 70% 训练集、30% 测试集划分数据集，利用网格搜索结合 5 折交叉验证对随机森林（RF）、极端梯度提升（XGBoost）、堆叠集成学习（Stacking）模型调参，以曲线下面积（AUC）、准确率（ACC）、F1 分数开展模型检验，借助自然断点法完成滑坡易发性分级制图。Stacking具备较高基础精度，负样本采样策略对模型性能影响显著；ACC、F1 分数在不同方案间差异较小，但AUC与空间制图效果区分明显。K-means 聚类采样可刻画非滑坡区环境异质性，与 Stacking模型耦合时综合性能最优，AUC达 0.93，ACC与 F1 分数最优，识别得到的高、极高易发区集中分布于研究区西北部中低山−丘陵过渡带，与实际滑坡集聚区高度吻合；缓冲区随机采样次之，低坡度约束采样模型效果最弱，易发性图件易出现高估与离散噪点。K-means 无监督聚类采样能够充分刻画群发滑坡区非滑坡环境异质性，结合Stacking可同时提升模型统计精度与空间地质合理性，可为南方丘陵山地降雨群发滑坡精细化易发性评价提供方法参考。
- 无监督聚类 /
- 负样本采样 /
- 集成学习 /
- 滑坡易发性 /
- 机器学习
Abstract:
Objective
Rainfall-triggered clustered landslides frequently occur in the hilly and mountainous regions of southern China, posing severe threats to local lives and property. Such geohazards feature short-term abrupt occurrence, multi-point concurrent failures, and strong spatial aggregation. Critically, landslide sites share highly similar geo-environmental conditions with adjacent stable slopes, which brings great challenges to landslide susceptibility modeling. Conventional negative sample sampling strategies often generate oversimplified non-landslide samples from environmentally distinct stable areas. These easy negative samples make machine learning models achieve seemingly high statistical scores while failing to capture subtle differences between unstable and stable slopes within landslide-prone zones, consequently producing widespread spatial misjudgments. Taking the clustered landslide event triggered by extreme rainfall in October 2023 across the Xinyi-Luoding border area, western Guangdong Province, China as the study object, this study aims to explore how coupling different negative sample sampling strategies with machine learning models affects the performance of landslide susceptibility assessment.
Methods
Based on pre- and post-event Planet satellite images, visual image interpretation was implemented to acquire 3792 positive landslide samples. Twelve evaluation factors were selected from five dimensions: meteorology and hydrology, topography and geomorphology, geology and soil, vegetation cover, and human activities. Spearman correlation analysis was applied to detect multicollinearity, and the elevation factor was eliminated due to strong collinearity. Under a 1∶1 positive-to-negative sample ratio and a unified 100-m buffer constraint around landslide boundaries, three types of negative sample datasets were established via buffer-based random sampling, low-slope constrained sampling and K-means unsupervised clustering sampling. The whole dataset was partitioned into a 70 % training subset and a 30 % testing subset. Grid search combined with five-fold cross-validation was adopted for hyperparameter optimization of random forest (RF), extreme gradient boosting (XGBoost), and Stacking models (using RF and XGBoost as base learners and logistic regression as the meta-learner). Model performance was quantified using AUC-ROC, accuracy (ACC), and F1-score, and the natural-breaks method was adopted to generate multi-grade landslide susceptibility maps.
Results
The results demonstrated that ensemble machine learning algorithms could achieve relatively high baseline performance, yet negative sample sampling strategies had a prominent influence on model outputs. Noticeably, ACC and F1-score values only showed slight discrepancies across different sampling schemes. Nevertheless, obvious differences were observed in AUC values and spatial pattern rationality of susceptibility maps. Benefiting from its capacity to characterize environmental heterogeneity within non-landslide domains, the K-means clustering sampling combined with the Stacking model obtained the optimal overall performance with an AUC of 0.93, accompanied by the highest ACC and F1-score. The derived high- and very-high-susceptibility zones were mainly distributed in the northwestern transition zone between low-to-middle mountains and hills, showing good agreement with the spatial pattern of actual landslide clusters. By comparison, buffer-based random sampling yielded moderate performance, while low-slope constrained sampling performed worst and tended to produce overestimated susceptibility and scattered mapping noise.
Conclusion
This study shows that the K-means-based unsupervised clustering sampling can sufficiently capture the environmental heterogeneity of non-landslide areas in clustered landslide areas. When integrated with the Stacking, it improves both statistical accuracy and spatial-geological rationality of susceptibility outputs. The proposed workflow provides a reliable methodological reference for refined susceptibility evaluation of rainfall-induced clustered landslides in hilly and mountainous regions across southern China.
- unsupervised clustering /
- negative sample sampling /
- ensemble learning /
- landslide susceptibility /
- machine learning

HTML全文

图 1 Planet 卫星影像与滑坡点分布

Figure 1. Planet satellite imagery and landslide-point distribution

下载: 全尺寸图片幻灯片

图 2 各环境评价因子空间分布图（TWI. 地形湿度指数；TPI. 地形位置指数；NDVI. 归一化植被指数；下同）

Figure 2. Spatial distribution of environmental evaluation factors

下载: 全尺寸图片幻灯片

图 3 不同采样方法负样本空间分布

Figure 3. Spatial distribution of negative samples obtained by different sampling methods

下载: 全尺寸图片幻灯片

图 4 基于手肘法的最优聚类簇数

Figure 4. Optimal number of clusters determined using elbow method

下载: 全尺寸图片幻灯片

图 5 评价因子斯皮尔曼相关性热力图

Figure 5. Spearman correlation heatmap of evaluation factors

下载: 全尺寸图片幻灯片

图 6 不同缓冲区距离条件下随机森林（RF）模型的受试者工作特征（ROC ）曲线（AUC为曲线下面积，下同）

Figure 6. Receiver operating characteristic curves of random forest model under different buffer distance conditions

下载: 全尺寸图片幻灯片

图 7 基于自然断点法的多组方案滑坡易发性评价结果

Figure 7. Landslide susceptibility assessment results of multiple schemes based on natural-breaks method

下载: 全尺寸图片幻灯片

图 8 不同采样−模型组合的模型精度评价

a～c为ROC 曲线；d～f为ACC，F1 分数。ACC为准确率，表征预测正确样本占总样本的比重；F1 分数为精确率与召回率的调和均值

Figure 8. Model accuracy evaluation for different sampling–model combinations

下载: 全尺寸图片幻灯片

表 1 环境评价因子数据来源

Table 1. Data sources of environmental evaluation factors

因子名称	分辨率	因子来源
降雨量	0.1°×0.1°	https://search.earthdata.nasa.gov
坡度	12.5 m	由数字高程模型（DEM）计算得到
坡向	12.5 m	由数字高程模型（DEM）计算得到
高程	12.5 m	https://www.gscloud.cn
TWI	12.5 m	由DEM计算得到
TPI	12.5 m	由DEM计算得到
土壤厚度	30 m	http://dcc.ngac.org.cn/
地层与岩性	1∶200000	http://dcc.ngac.org.cn/
土地利用类型	30 m	https://www.ncdc.ac.cn/
NDVI	30 m	https://www.ncdc.ac.cn/
距房屋距离	—	https://www.gscloud.cn
距道路距离	—	https://www.gscloud.cn

下载: 导出CSV

表 2 不同负样本采样策略下各模型最优超参数

Table 2. Optimal hyperparameters of models under different negative sample sampling strategies

模型	采样策略	超参数
RF	非滑坡区采样	n_estimators=500; max_depth=20; max_features=sqrt; min_samples_split=2; min_samples_leaf=1; criterion='gini'
	低坡度区采样	n_estimators=400; max_depth=15; max_features=sqrt; min_samples_split=2; min_samples_leaf=5; criterion='gini'
	K-means聚类采样	n_estimators=800; max_depth=25; max_features=sqrt; min_samples_split=2; min_samples_leaf=1; criterion='gini'
XGBoost	非滑坡区采样	n_estimators=300; learning_rate=0.05; max_depth=6; subsample=0.8; colsample_bytree=0.8; gamma=0.1
	低坡度区采样	n_estimators=300; learning_rate=0.1; max_depth=4; subsample=0.8; colsample_bytree=0.8; gamma=0.1
	K-means聚类采样	n_estimators=1000; learning_rate=0.01; max_depth=8; subsample=0.7; colsample_bytree=0.8; gamma=0.1
Stacking	非滑坡区采样	Meta_Learner=Logistic Regression; C=1.0; penalty=l2; solver='lbfgs'
	低坡度区采样	Meta_Learner=Logistic Regression; C=1.0; penalty=l2; solver='lbfgs'
	K-means聚类采样	Meta_Learner=Logistic Regression; C=1.0; penalty=l2; solver='lbfgs'
注：n_estimators 为决策树数量；max_depth 为树最大深度；max_features 为节点分裂候选特征数；min_samples_split 为节点分裂最小样本数；min_samples_leaf 为叶节点最小样本数；criterion 为分裂准则；learning_rate 为学习率；subsample 为样本采样比例；colsample_bytree 为特征采样比例；gamma 为节点分裂最小损失下降阈值；Meta_Learner 为元学习器；C 为逻辑回归正则化系数；penalty 为正则化类型；solver 为优化求解器。RF为随机森林；XGBoost为极端梯度提升；Stacking为堆叠集成学习；下同

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