一种基于遗传算法的滑坡蠕滑位移预测模型

冯谕; 曾怀恩; 涂鹏飞

doi:10.16031/j.cnki.issn.1003-8035.202209038

一种基于遗传算法的滑坡蠕滑位移预测模型

doi: 10.16031/j.cnki.issn.1003-8035.202209038

冯谕^2,,
曾怀恩^{1, 2, 3},
涂鹏飞^{2, 3, ,}

1.
湖北长江三峡滑坡国家野外科学观测研究站，湖北宜昌　443002
2.
三峡大学土木与建筑学院，湖北宜昌　443002
3.
湖北省水电工程施工与管理重点实验室（三峡大学），湖北宜昌　443002)

基金项目: 国家自然科学基金资助项目(42074005)；湖北省地质局2021年度科技项目(KJ2021-16)

详细信息

作者简介:
冯谕：冯　谕（1998-），男，研究生，研究方向:滑坡监测数据处理研究。E-mall：2044207612@qq.com

通讯作者:
涂鹏飞（1965-），硕士，教授，研究方向：地质灾害监测与预警研究。E-mall：13872539886@163.com

中图分类号: 中图分类号: 文献标识码:A 文章编号:
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出版历程
- 网络出版日期: 2023-10-17

A prediction model for landslide creep displacement based on Genetic Algorithm

Yu FENG^2
,,
Huaien ZENG^{1, 2, 3},
Pengfei TU^{2, 3
, ,}

1.
National Field Observation and Research Station of Landslides in the Three Gorges Reservoir Area of Yangtze River, Yichang, Hubei　443002, China
2.
College of Civil Engineering & Architecture, China Three Gorges University, Yichang, Hubei　443002, China
3.
Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang, Hubei　443002, China

摘要

摘要: 滑坡位移预测是预报滑坡灾害的重要依据，以往的滑坡位移预测模型多数为时间序列预测模型、BP神经网络预测模型、Gaussian拟合预测模型以及其他一些非线性预测模型。这些滑坡位移预测模型在建立上缺乏力学理论支撑，对不同力学特性产生的滑坡位移预测分析上没有针对性。文中针对力学特性为重力蠕变型滑坡位移的预测，提出一种基于遗传优化算法的滑坡蠕滑位移非线性预测模型。以鲁家坡滑坡东侧J05监测点的累计水平位移为例，划定测试区域与预测区域进行模型预测分析，并将新模型预测结果与Gaussian拟合预测模型、BP神经网络预测模型预测结果进行对比分析。结果表明，相较于传统预测模型，新模型的预测效果有所提升，有一定的工程价值与实践价值。
- 滑坡预测 /
- 遗传算法 /
- 蠕滑位移 /
- 函数模型 /
- 对比分析
Abstract: Landslide displacement prediction is an important basis of predicting landslide disasters. Most of the previous landslide displacement prediction models include time series prediction models, BP neural network prediction models, Gaussian fitting prediction models, and various other nonlinear prediction models. However, these landslide displacement prediction models lack the foundation of mechanical theory in the establishment and have no pertinence in the prediction and analysis of landslide displacement resulting from diverse mechanical properties. In this paper, a nonlinear prediction model of landslide creep displacement based on Genetic optimization Algorithm is proposed for the prediction of Gravity Creep landslide displacement. Using the cumulative horizontal displacement data from monitoring point J05 on the eastern side of the Lujiapo landslide as a case study, the test area and prediction area are delimited for model prediction analysis. The results of the new model are compared with those of Gaussian fitting model and BP neural network model. The results indicate that, in comparison to the traditional prediction models, the new model exhibits improved predictive performance, offering a certain engineering value and practical value.
- Landslide Prediction /
- Genetic Algorithm /
- Creep Displacement /
- Function Model /
- Comparative Analysis

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图 1 遗传算法基本流程

Figure 1. Basic Flow chart of Genetic Algorithm

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图 2 适应度函数程序设计技术路线

Figure 2. Technical roadmap for Fitness Function Program design

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图 3 鲁家坡滑坡全貌

Figure 3. Panoramic view of the Lujiapo landslide

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图 4 不同应力条件下的蠕变曲线

Figure 4. Creep Curves Under Different Stress Conditions

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图 5 监测点水平位移-时间散点分布

Figure 5. Scatter Distribution of Horizontal Displacement vs. Time at Monitoring Points

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图 6 监测点水平位移-时间散点区域分布

Figure 6. Scatter Area Distribution of Horizontal Displacement vs. Time at Monitoring Points

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图 7 测试数据统一决定系数迭代过程

Figure 7. Iteration process of Determination for the Test Data Iterative Process

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图 8 进化点函数拟合曲线

Figure 8. Fitting Curve of the Evolution Point Function

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图 9 测试数据拟合结果

Figure 9. Test Data fitting results

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图 10 三种预测模型的测试数据拟合曲线

Figure 10. Test Data Fitting Curves of Three Prediction Models

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图 11 三种预测模型的原始数据拟合曲线

Figure 11. Fitting Curves of Original Data for Three Prediction Models

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图 12 三种预测模型的预测数据拟合曲线

Figure 12. Fitting Curves of Predicted Data for Three Prediction Models

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图 13 三种预测模型绝对误差统计

Figure 13. Statistical Analysis of Absolute Errors for Three Prediction Models

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表 1 遗传算法迭代参数

Table 1. Iteration parameter of Genetic Algorithm

参数名称	符号
种群数量	npop
迭代次数	maxit
染色体数	nv
交叉算子	npc
变异算子	nmu
子代数量	nc

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表 2 遗传算法迭代参数设定表

Table 2. Genetic Algorithm iteration parameter configuration table

迭代参数	数值
npop	30
maxit	500
nvar	23
npc	0.9
nmu	0.1
nc	27

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表 3 测试数据拟合模型迭代进化表

Table 3. Iterative Evolution Table of Test Data Fitting Models

进化阶段	迭代次数	统一决定系数R_NL
0	1	0.828 0
1	101	0.839 6
2	106	0.839 8
3	151	0.840 6
4	221	0.845 0

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表 4 拟合模型各进化阶段求解参数

Table 4. Parameters solutions at each Evolution Stage of the Fit Model

参数		a₁	b₁	c₁	d₁	D	R_NL
进化阶段	0	+388.3	−1.351E-02	−3.786E+01	+2.179E-02	−383.2	0.828 0
	1	+421.8	−1.937E-01	−1.003E-03	−1.770E-02	−351.0	0.839 6
	2	+454.2	−9.954E-02	−1.257E-03	−5.410E-03	−411.1	0.839 8
	3	+136.4	−8.182E-01	−1.667E-03	−2.010E-02	−72.96	0.840 6
	4	+6.379	−2.524E+04	−4.264E-02	+1.620E-02	+3.167	0.845 0

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表 5 三种预测模型预测区域统计学指标

Table 5. Statistical Metrics for Prediction Regions of Three Prediction Models

预测方法		遗传算法拟合预测	高斯拟合预测	BP神经网络预测
统计学指标	R_NL	0.897 5	0.817 4	0.888 2
	RMSE	2.596 7	4.639 1	2.790 4
	FR	0.994 9	0.992 7	0.994 8
	R²	0.991 2	−1.216 2	0.826 7

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