滑坡多发区第四系堆积层厚度智能预测与影响因素分析

孟佳佳; 吴益平; 柯超; 苗发盛

doi:10.16031/j.cnki.issn.1003-8035.202202008

滑坡多发区第四系堆积层厚度智能预测与影响因素分析

中国地质大学（武汉）工程学院，湖北武汉　430074

基金项目: 国家自然科学基金项目（41977244；42007267）

详细信息

作者简介: 孟佳佳（1999-），女，河南开封人，硕士研究生，主要从事地质灾害预测预报研究。E-mail：jjm@cug.edu.cn

通讯作者: 吴益平（1971-），女，浙江杭州人，教授，博士生导师，从事地质灾害预测预报与风险评价研究。E-mail：ypwu@cug.edu.cn

中图分类号: P642.22

收稿日期: 2022-02-10

修回日期: 2022-04-07

刊出日期: 2023-04-25

Intelligent prediction and analysis of influencing factors of Quaternary accumulation layer thickness in landslide-prone areas: A case study in the Tiefeng area of Wanzhou District, Chongqing City

Faculty of Engineering, China University of Geosciences（Wuhan）, Wuhan, Hubei　430074, China

More Information

Corresponding author: WU Yiping, ypwu@cug.edu.cn

Received Date: 10 February 2022

Revised Date: 07 April 2022

Publish Date: 25 April 2023

摘要

摘要:
堆积层厚度是区域工程地质调查的基础数据，对识别滑坡具有重要意义，在滑坡的稳定性评价和风险评估中发挥着重要的作用。传统的空间插值方法对外界因素考虑不足，难以满足精度要求。文章以三峡库区万州区铁峰乡为研究对象，在实地调查的基础上，总结提出了堆积层的成因模式，并据此添加区域堆积层厚度控制点；基于Apriori算法挖掘影响因子与堆积层厚度分布之间的关联准则，利用已知样本点采用三种机器学习方法建立模型，最后将模型应用于整个区域得到堆积层厚度分布图，并对结果进行对比分析。结果表明：选取的13种影响因子中6种因子与堆积层厚度表现出较强的相关性；数据挖掘显示坡度和地形起伏是影响堆积层厚度空间分布差异的主要因素。3种机器学习模型中GWO-SVM模型的预测结果与实际最为吻合。研究结果揭示了区域第四系堆积层的成因机制，为机器学习技术在堆积层厚度预测领域奠定了基础。
- 堆积层厚度 /
- 空间预测 /
- 成因模式 /
- 数据挖掘 /
- 机器学习
Abstract:
Accumulation layer thickness is the basic data of regional engineering geological investigation, which is of great significance to identifying landslides and plays an important role in landslide stability evaluation and risk assessment. The traditional interpolation method does not take into account external factors, and it is difficult to meet the accuracy requirements. This study takes the Tiefeng Township of Wanzhou District in the Three Gorges Reservoir as the research object, summarizing the formation model of the accumulation layer and regional accumulation layer thickness control points are added accordingly. The Apriori algorithm is used to mine the association rules between the influence factors and the thickness distribution of accumulation layers. Three machine learning methods are used to build the model by using the known sample points, which are applied to the entire area to obtain the thickness distribution map of the accumulation layer, then compare the results. Results show that 6 of the 13 selected influence factors have a strong correlation with the accumulation layer thickness, and slope and topographic undulation are the main factors that cause the difference in the spatial distribution of accumulation layer thickness. Among the three machine learning methods, the prediction result of the GWO-SVM model is the most consistent with the actual situation. The results reveal the formation mechanism of regional quaternary accumulation layer and lay a foundation for machine learning in the field of accumulation layer thickness prediction.
- accumulation layer thickness /
- spatial prediction /
- formation model /
- data mining /
- machine learning

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图 1 Apriori算法示意图

Figure 1.

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图 2 万州区位置及铁峰乡滑坡分布图

Figure 2.

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图 3 残坡积示意图

Figure 3.

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图 4 顺向坡远程滑动堆积示意图

Figure 4.

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图 5 顺向坡近距离滑动堆积示意图

Figure 5.

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图 6 顺向坡侧向崩塌堆积示意图

Figure 6.

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图 7 软弱相间岩层分布模式示意图

Figure 7.

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图 8 各因子统计图

Figure 8.

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图 9 各模型测试结果对比（测试样本）

Figure 9.

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图 10 各模型的区域堆积层厚度预测结果

Figure 10.

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表 1 各因子与堆积层厚度相关性指标

Table 1. Correlation index between each factor and accumulation layer thickness

因子		GRA	MIC
因子1	坡度	0.6923	0.6977
因子2	坡向	0.6878	0.5285
因子3	流向	0.8673	0.4696
因子4	高程	0.7050	0.0752
因子5	汇水条件	0.6349	0.0417
因子6	地形起伏	0.6856	0.6933
因子7	地层	0.7546	0.0486
因子8	距水系距离	0.6366	0.0525
因子9	距陡崖距离	0.6063	0.0637
因子10	剖面曲率	0.6953	0.2141
因子11	平面曲率	0.6783	0.0784
因子12	斜坡结构	0.6364	0.3271
因子13	植被覆盖	0.6440	0.0943

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表 2 各因子属性指标

Table 2. Attribute indicators of each factor

因子		属性	类别
因子1	坡度	0～20°	F11
		20°～40°	F12
		>40°	F13
因子2	坡向	337.5°～67.5°	F21
		67.5°～157.5°	F22
		157.5°～247.5°	F23
		247.5°～337.5°	F24
因子3	流向	1&2	F31
		4&8	F32
		16&32	F33
		64&128	F34
因子6	地形起伏	0～2	F61
		2～4	F62
		>4	F63
因子10	剖面曲率	0～20	F101
		20～40	F102
		40～60	F103
		>60	F104
因子12	斜坡结构	顺向坡	F121
		逆向坡	F122
		斜交坡	F123

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表 3 堆积层厚度关联准则

Table 3. Association criterion of accumulation layer thickness

规则 ID	规则	支持度/%	置信度/%
1	YZ3=F31 & YZ6=F62 & YZ2=F21HD5	0.08	100.00
2	YZ3=F31 & YZ2=F21 & YZ1=F12HD5	0.08	100.00
3	YZ2=F22 & YZ3=F34 & YZ1=F11HD3	0.08	100.00
4	YZ2=F22 & YZ3=F34 & YZ6=F61HD3	0.08	100.00
5	YZ3=F34 & YZ1=F12 & YZ2=F24HD2	0.17	100.00
6	YZ2=F23 & YZ10=F102 & YZ1=F11HD2	0.08	100.00
7	YZ10=F102 & YZ2=F22 & YZ1=F11HD2	0.08	100.00
8	YZ3=F31 & YZ6=F63HD1	18.58	100.00
9	YZ3=F31 & YZ1=F13 & YZ6=F63HD1	17.18	100.00
10	YZ2=F22 & YZ1=F13 & YZ6=F63HD1	17.01	100.00

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表 4 各模型预测精度对比

Table 4. Comparison of prediction accuracy of each model

预测模型	MSE	RMSE	Q	C	平均厚度/m
PSO-SVM	401.22	1.42	0.47	0.79	1.49
GA-SVM	198.57	1.00	0.23	0.76	1.76
GWO-SVM	152.24	0.87	0.19	0.83	1.83

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