Composite Prediction of Landslide Deformation Based on Support Vector Machine and BP Neural Network

YE Chao; HAO Fujun

doi:10.13961/j.cnki.stbctb.2016.03.057

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Composite Prediction of Landslide Deformation Based on Support Vector Machine and BP Neural Network

- Composite Prediction of Landslide Deformation Based on Support Vector Machine and BP Neural Network
- Bulletin of Soiland Water Conservation Vol. 36, Issue 3, Pages: 332-337(2016)
- 作者机构：
  
  陕西铁路工程职业技术学院,陕西,渭南,714000
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2016.03.057
  CLC：
- Published：2016
- 稿件说明：
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YE Chao, HAO Fujun. Composite Prediction of Landslide Deformation Based on Support Vector Machine and BP Neural Network[J]. Bulletin of Soiland Water Conservation, 2016, 36(3): 332-337.
DOI：

YE Chao, HAO Fujun. Composite Prediction of Landslide Deformation Based on Support Vector Machine and BP Neural Network[J]. Bulletin of Soiland Water Conservation, 2016, 36(3): 332-337. DOI： 10.13961/j.cnki.stbctb.2016.03.057.

摘要

［目的］探讨复合式组合预测模型对滑坡两变形时间序列的预测效果，为滑坡的变形预测提供一种新的思路。［方法］基于支持向量机和BP神经网络，构建滑坡位移序列和速率序列的复合式预测模型，首先，对滑坡环境因素进行分析，提取其基本信息；其次，利用2种预测方法构建回归结构预测模型和多因素预测模型，并对两时间序列进行一重预测；最后，利用BP神经网络对一重预测结果进行了二重组合优化。［结果］滑坡库水位与滑坡两变形序列均具有较大的相关性，滑坡的稳定性很大程度上会出现周期性疲劳减弱的可能，且通过对滑坡变形的复合式预测。［结论］该方法的相对预测误差均较小，很大程度上提高了滑坡变形的预测精度和稳定性，证明了该预测模型的有效性。

Abstract

[Objective] To explore the effect of a compound predicting model in forecasting the deformation time series of landslide in order to provide a new way for the landslide deformation prediction.[Method] Based on support vector machine and BP neural network

a compound predicting model of landslide displacement sequence and rate series was established. The basic information of landslide was analyzed

and extracted. The regression and multi-factor models were constructed by using two kinds of predicting methods

and two time series was predicted. The BP neural network was used to optimize the results.[Result] There was a great correlation between the water level of the landslide reservoir and the two deformation sequence. The stability of the landslide was likely to be weakened by periodic fatigue

and it could be predicted by the compound prediction model of landslide deformation.[Conclusion] The relative prediction error of this study is small

which greatly improves the prediction accuracy and stability of the landslide deformation

and proves the validity of the prediction model.

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references

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