基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型

徐文兵; 王国河; 王生; 伍明兆; 姚清河

doi:10.13471/j.cnki.acta.snus.2019.07.27.2019B071

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基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型

研究论文 | 更新时间：2023-11-01

- 基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型
- Displacement prediction model of Egongdai landslide in Lechangxia based on PSO-SVM algorithms
- 中山大学学报(自然科学版) 2020年59卷第5期页码：57-65
- 作者机构：
  
  1.中山大学应用力学与工程系，广东广州 510006
  2.中广核研究院有限公司，广东深圳 518040
- 作者简介：
  
  徐文兵( 1994 年生)，男; 研究方向: 流体力学; E-mail: xuwb9@ mail2.sysu.edu.cn
  姚清河( 1980 年生)，男; 研究方向: 流体力学; E-mail: yaoqhe@ mail. sysu. edu. cn
- 基金信息：
  
  广东省促进经济高质量发展专项资金海洋经济发展重大项目（GDOE[2019]A01）
- DOI：10.13471/j.cnki.acta.snus.2019.07.27.2019B071
  中图分类号： TU432
- 纸质出版日期：2020-09-25，
  
  收稿日期：2019-07-27，
扫描看全文
徐文兵,王国河,王生等.基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型[J].中山大学学报(自然科学版),2020,59(05):57-65.

XU Wenbing,WANG Guohe,WANG Sheng,et al.Displacement prediction model of Egongdai landslide in Lechangxia based on PSO-SVM algorithms[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):57-65.
徐文兵,王国河,王生等.基于PSO-SVM算法的乐昌峡鹅公带滑坡体位移预测模型[J].中山大学学报(自然科学版),2020,59(05):57-65. DOI： 10.13471/j.cnki.acta.snus.2019.07.27.2019B071.

XU Wenbing,WANG Guohe,WANG Sheng,et al.Displacement prediction model of Egongdai landslide in Lechangxia based on PSO-SVM algorithms[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):57-65. DOI： 10.13471/j.cnki.acta.snus.2019.07.27.2019B071.

摘要

以乐昌峡鹅公带滑坡体作为研究对象，考虑日降雨量、渗压对边坡变形的影响，建立了BP、SVM、PSO-BP、PSO-SVM四种滑坡体变形预测模型。从乐昌峡安全检测系统导出近4年研究数据，筛选使用其中410组数据进行训练，取30组变形位移作为输出，分析后发现PSO-SVM模型为最佳模型。以PSO-SVM模型为研究对象，对粒子群算法迭代次数、种群规模、速度位置相关系数(

)等因素进行研究，得知三者分别为100、30、0.5时得到最优的PSO-SVM模型，此时的RMSE、MAPE、

分别为0.202 mm、0.589%、0.985。相对于大型有限元仿真软件、多元线性回归模型等传统方法，文章所提出的预测模型可以减少计算成本；在面对非线性问题时也能够获得更好的处理效果。

Abstract

Taking Lechangxia Egongdai landslide as the research object

the influence of daily rainfall and osmotic pressure on slope deformation is considered. By establishing BP

SVM

PSO-BP

PSO-SVM four landslide body deformation prediction models

the research data of the last 4 years is derived from the Lechangxia safety inspection system

and 410 sets of data are used for training through screening

and 30 sets of deformation displacements are taken as an output

after analysis

the PSO-SVM model is found to be the accurate model. Taking the PSO-SVM model as the basic model

the factors such as the number of iterations of the particle swarm algorithm

the population size

and velocity position correlation coefficient (

) are studied

and the best PSO-SVM is obtained when the three are 100

and 0.5

respectively. In this model

the RMSE

MAPE

and

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49494177&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=49494176&type=

3.38666677

2.96333337

are 0.202 mm

0.589%

and 0.985

respectively. Compared with traditional methods such as large-scale finite element simulation software and multiple linear regression models

the prediction model proposed in this article can reduce the computational cost and obtain better processing results in the face of nonlinear problems. At the same time

it can reduce the lack of fitting accuracy caused by incomplete factor analysis.

关键词

鹅公带滑坡体位移预测PSO-SVM模型参数寻优

Keywords

Egongdai landslidedisplacement predictionmodel of PSO-SVMparameter optimization

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