Prediction of landslide displacements through multimode support vector machine model

LIN Da-chao; AN Feng-ping; GUO Zhang-lin; ZHANG Li-ning

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Prediction of landslide displacements through multimode support vector machine model

更新时间：2026-06-25

- Prediction of landslide displacements through multimode support vector machine model
- Vol. 32, Issue S1, (2011)
- 作者机构：
  
  1. 华北科技学院土木工程系,北京,101601
  2. 河北工程大学经济管理学院,河北,邯郸,056038
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU457
- Online First：16 May 2011，
  
  Published：2011
- 稿件说明：
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LIN Da-chao, AN Feng-ping, GUO Zhang-lin, et al. Prediction of landslide displacements through multimode support vector machine model[J]. 2011, 32(S1).
DOI：

LIN Da-chao, AN Feng-ping, GUO Zhang-lin, et al. Prediction of landslide displacements through multimode support vector machine model[J]. 2011, 32(S1). DOI：

摘要

将支持向量机（support vector machine，SVM）方法与信号分析中的经验模态分解（empirical mode decomposition， EMD）方法相匹配，提出了一种通过多模态支持向量机函数回归分析建模预测滑坡位移的理论方法。以边坡位移历史观测数据为基础，应用EMD方法获得滑坡形成过程中位移演化的几个特征时间模态，构成了多模态信息统计学习样本，确定了边坡位移演化的自适应多尺度变化信息。对应于每个经验模态的位移变化信息，引入了多模态SVM建模方法，然后合成不同经验模态下边坡位移的计算结果，得到滑坡位移的预测值。以卧龙寺新滑坡和新滩滑坡的监测数据为基础的理论预测结果表明，与采用遗传算法的神经网络方法的预测结果相比，支持向量机经验模态方法具有更强的预测能力，理论预测结果与实际监测值具有很好的一致性

Abstract

A theoretical approach to predict landslide displacements

in which the support vector machine (SVM) method is coupled with the empirical mode decomposition (EMD) in signal processing

is suggested through the multimode SVM function regression modeling. On the basis of the historically recorded data of displacements for a slope

several intrinsic time modes for the evolutionary of displacements are obtained in the process of landslide forming by using EMD method; and they are components of statistical learning samplings with multimode information

determining the multiscale adaptive information of slope displacements varying with time. Corresponding to the information of displacement evolutionary in each empirical mode

the multimode SVM modeling method is introduced; and then the estimations of landslide displacements are obtained by the composition summing the results of slope displacements from different empirical modes. The theoretical results calculated by the proposed approach based on the monitoring data of Wolongsi new landslide and Xintan landslide show that the applications of the SVM method coupled with the EMD method

comparing with those of the genetic algorithm neural network method

have a more powerful ability for landslide displacement prediction; and the theoretical estimations are identical with the monitoring data very well.

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