基于多影响因子的青海省堆积层滑坡位移预测模型

王克强; 黎明; 李良龙; 李迎朋; 马永刚; 张炜怡; 徐红剑; 章广成

doi:10.19509/j.cnki.dzkq.tb20250339

基于多影响因子的青海省堆积层滑坡位移预测模型

doi: 10.19509/j.cnki.dzkq.tb20250339

1.
青海工程勘察院有限公司，西宁 810000
2.
中国地质大学（武汉）工程学院，武汉 430074
3.
中安国泰（北京）科技发展有限公司，北京 102209

基金项目: 青海省海东市乐都区瞿昙镇滑坡群成生机理与孕灾模式研究项目（KH2461060）

详细信息

作者简介:
王克强：E-mail：270062498@qq.com

通讯作者:
E-mail：zhangguangc@cug.edu.cn

中图分类号: P642.22
计量
- 文章访问数: 205
- PDF下载量: 33
- 被引次数: 0
出版历程
- 收稿日期: 2025-07-20
- 录用日期: 2025-11-26
- 修回日期: 2025-11-20
- 网络出版日期: 2025-12-15

Displacement prediction model of colluvial landslides in Qinghai Province based on multiple influencing factors

1.
Qinghai Engineering Survey Institute Co., Ltd., Xining 810000, China
2.
Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3.
Cathay Safety Technology Co., Ltd., Beijing 102209, China

More Information

Author Bio:
E-mail：270062498@qq.com

Corresponding author: E-mail：zhangguangc@cug.edu.cn

摘要

摘要:
滑坡作为我国分布广泛的地质灾害之一，其分布范围已经涉及到西部地区，而青海地区地貌单元复杂，山脉成群，为滑坡的产生提供良好的地质条件。深入研究该区域典型滑坡的成因机制与影响因素可以为滑坡治理与灾害预测提供有效的理论支撑，减轻人员伤亡和经济损失。以青海省乐都区瞿昙镇韩家村堆积层滑坡群作为研究对象，基于野外勘察与监测数据，对该滑坡群的宏观变形特征与成因机制进行分析，采用小波相干分析法研究了降雨、温度与滑坡变形时序之间的相关性。选择温度和降雨等作为自变量因子，针对基于线性回归集成模型中的各模型预测的位移数据，采用加权求和法获得了该滑坡GNSS2监测点的位移预测值。结果表明，韩家村滑坡群平均年变形量为8.5 mm，属蠕变滑坡，受温度和降雨影响呈现阶跃型变形特征。降雨与累计位移变形呈正相关趋势，即夏季集中降雨期间滑坡位移突增，雨季后则又趋于稳定，且位移具有滞后性；温度与累计位移呈负相关趋势，即冬季因温度下降产生冻胀作用，导致坡体内土颗粒之间的孔隙水结冰膨胀，滑坡变形量增加，春季因融沉作用导致堆积层出现回弹现象。集成模型对 GNSS2监测点位移拟合优度达 0.990，可较精确地预测该滑坡群的位移变形。本研究所建多元线性回归集成模型预测精度优异，可用于高寒地区同类蠕变型堆积层滑坡短期位移预测。
- 堆积层滑坡 /
- 蠕变变形 /
- 小波相干分析 /
- 降雨−冻融作用 /
- 多元线性回归集成模型 /
- 位移预测 /
- 青海省
Abstract:
Objective
Landslides, as one of the most prevalent geological hazards in China, are widely distributed and have also extended into the western regions. The Qinghai region is characterized by complex geomorphic units and clustered mountain systems, which provide favorable geological conditions for the initiation and development of landslides. A comprehensive investigation into the formation mechanisms and influencing factors of representative landslides in this region can provide essential theoretical support for landslide prevention, mitigation, and hazard forecasting, thereby reducing casualties and economic losses.
Methods
This study focused on the accumulation landslide group of Hanjiacun, Qutan Town, Ledu District, Qinghai Province. Based on field investigations and monitoring data, the macroscopic deformation characteristics and formation mechanisms of the landslide group were systematically analyzed. Furthermore, the correlation between rainfall, temperature, and the deformation time series was examined using wavelet coherence analysis. Temperature and rainfall were selected as the principal external variables. A linear regression ensemble model was employed, in which the predicted displacements from individual models were combined through a weighted summation approach to estimate the displacement at the GNSS2 monitoring point of the landslide.
Results
The results indicated that the Hanjiacun landslide group exhibited an average annual deformation rate of approximately 8.5 mm, classifying it as a typical creep-type landslide. Its displacement demonstrated a step-like deformation pattern under the influence of both rainfall and temperature. Specifically, rainfall showed a positive correlation with cumulative displacement, with abrupt increases observed during periods of concentrated summer rainfall, followed by stabilization after the rainy season, while a lag effect was also evident. Temperature, in contrast, was negatively correlated with cumulative displacement. As temperatures decreased in winter, frost heave was induced by the freezing and volumetric expansion of pore water within the soil matrix of the slope, resulting in an increase in landslide deformation. With the onset of spring, thaw settlement caused a rebound phenomenon in the accumulation layer. The ensemble model achieved a goodness-of-fit of 0.990 for displacement prediction at the GNSS2 monitoring point and can accurately predict the landslide deformation of the landslide group.
Conclusion
The established multiple linear regression ensemble model shows excellent prediction accuracy and can be applied to short-term displacement forecasting of similar creep-type colluvial landslides in alpine cold regions.
- colluvial landslide /
- creep deformation /
- wavelet coherence analysis /
- rainfall-freeze-thaw effect /
- multiple linear regression ensemble model /
- displacement prediction /
- Qinghai Province

HTML全文

图 1 研究区位置示意(a)与气象数据(b～c)（DEM. 数字高程模型）

Figure 1. Study area (a) and meteorological data map (b-c)

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图 2 韩家村滑坡群地形地貌图(a～d)和宏观变形(e)图

Figure 2. Topographic map (a-d) and macroscopic deformation map (e) of Hanjiacun landslide group

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图 3 韩家村滑坡群岩土体

Figure 3. Rock and soil mass in Hanjiacun landslide group

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图 4 韩家村滑坡群3−3'工程地质剖面图（剖面位置见图2e）

Figure 4. Engineering geological profile of Hanjiacun landslide group 3-3'

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图 5 韩家村滑坡群土洞发育情况

Figure 5. Development of soil caves in Hanjiacun landslide group

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图 6 韩家村滑坡群气候数据(a, b)与各监测点累计位移曲线(c～f)分析

Figure 6. Analysis of climate data (a-b) and cumulative displacement curves of each monitoring point (c-f) of Hanjiacun landslide group

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图 7 小波相干分析方向图例（π. 周期）

Figure 7. Legend of wavelet coherence analysis direction

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图 8 降雨与滑坡群各监测点累计位移关系的小波结果

图中灰色区域为影响锥外区域，表示该部分小波分析结果受边界效应影响较大，其能量分布和相干性解释可靠性较低，因此主要分析影响锥以内的显著区域；图中的黑色粗实线圈闭区域表示通过显著性检验的区域，通常是95% 置信水平显著区域；XWT. 交叉小波变换；WTC. 小波相干变换；箭头为降雨及累计位移关系2个时间序列在频域中的相位关系，详见图7；下同

Figure 8. Wavelet results of relationship between rainfall and displacement at each monitoring point of landslide group

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图 9 温度与滑坡群各监测点累计位移关系的小波结果

Figure 9. Wavelet results of relationship between temperature and displacement at each monitoring point of landslide group

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图 10 滑坡变形阶段划分

Figure 10. Division of landslide deformation stages

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图 11 GNSS2监测点残差值(a)与累计位移变形量预测结果(b)分析（σ. 标准差）

Figure 11. Analysis of residual values (a) and cumulative displacement deformation prediction results (b) of GNSS2 monitoring points

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表 1 滑坡运动速率分类^[61]

Table 1. Classification of landslide movement rates^[61]

速度分类	属性	运动速度/(mm·s⁻¹)	典型运动率
7	极其快	5×10³	5 m/s
6	较快	5×10¹	3 m/min
5	快速	5×10⁻¹	1.8 m/h
4	中等	5×10⁻³	13 m/month
3	缓慢	5×10⁻⁵	1.6 m/a
2	较缓慢	5×10⁻⁷	16 mm/a
1	极其缓慢	—	—

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表 2 各影响因素的重要特征数据

Table 2. Important characteristic data of each influencing factor

影响因素	特征数据选择	权重β_j	列表排名
时间	天数	0.676	7
	傅里叶年周期正弦项	0.101	18
	傅里叶天周期余弦项	−0.094	19
	傅里叶月周期余弦项	−0.056	20
位移	位移−滞后1 d	3.198	1
	位移−滞后2 d	3.040	2
	趋势项	2.927	3
	季节周期项	0.866	4
降雨	每14 d累计降雨总和	−0.013	29
	有效降雨−滞后1 d	−0.012	30
	每3 d累计降雨总和	0.011	31
	有效降雨	−0.007	34
温度	每30 d滑动平均温度值	−0.041	22
	每14 d滑动平均温度值	0.033	23
	温度	0.016	26
	每7 d滑动平均温度值	0.015	27

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表 3 4种线性回归模型相关参数

Table 3. Related parameters of four linear regression models

	训练模型				总值
	Ridge	Lasso	ElasticNet	Ridge_CV	总值
权重系数	0.395	0.006	0.006	0.594	1
截距β₀	13.355	13.286	12.984	13.285	13.311

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表 4 各数据集性能评价

Table 4. Performance evaluation of each dataset

	数据集			全量预测
	训练集	验证集	测试集	全量预测
拟合优度R²	0.999	0.931	0.965	0.990
均方根误差RMSE/mm	0.102	0.253	0.337	0.204
标准差σ/mm	0.182	0.062	0.069	0.210

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