基于相空间重构和小波分析-粒子群向量机的滑坡地下水位预测

黄发明; 殷坤龙; 张桂荣; 周春梅; 张俊

doi:10.3799/dqkx.2015.105

Volume 40 Issue 7

Jul. 2015

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Article Navigation > Earth Science > 2015 > 40(7): 1254-1265

Huang Faming, Yin Kunlong, Zhang Guirong, Zhou Chunmei, Zhang Jun, 2015. Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine Optimized by PSO Algorithm. Earth Science, 40(7): 1254-1265. doi: 10.3799/dqkx.2015.105

Citation:

Huang Faming, Yin Kunlong, Zhang Guirong, Zhou Chunmei, Zhang Jun, 2015. Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine Optimized by PSO Algorithm. Earth Science, 40(7): 1254-1265. doi: 10.3799/dqkx.2015.105

Citation:

Huang Faming, Yin Kunlong, Zhang Guirong, Zhou Chunmei, Zhang Jun, 2015. Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine Optimized by PSO Algorithm. Earth Science, 40(7): 1254-1265. doi: 10.3799/dqkx.2015.105

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Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine Optimized by PSO Algorithm

doi: 10.3799/dqkx.2015.105

1.
Intitute of Geological Survey, China University of Geosciences, Wuhan 430074, China
2.
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
3.
Nanjing Hydraulic Research Institute, Nanjing 210029, China
4.
School of Resource and Civil Engineering, Wuhan Institute of Technology, Wuhan 430073, China

Received Date: 2014-11-18
Publish Date: 2015-07-15

Abstract

Abstract

It is of great significance to predict the dynamic evolution process of landslide underground water level for landslide stability analysis. For the problem that the evolution process of groundwater level in reservoir landslide is a highly non-linear and non-stationary time series affected by many factors, to predict landslide groundwater level time series, a coupling model based on phase space reconstruction and wavelet analysis-support vector machine (WA-PSVM) optimized by particle swarm optimization is proposed. Firstly, the groundwater level time series was decomposed into several different frequency components to transform the non-stationary groundwater level time series into stationary time series. Secondly, the PSVM model was established for each component prediction based on the phase-space reconstruction. At last, the final prediction result was obtained by adding the predicted values of all frequency components. Taking daily average groundwater level time series of STK-1 hydrology hole on Sanzhouxi Landslide in the Three Gorges Reservoir Area for example, the influencing factors of landslide groundwater level fluctuation were analyzed and WA-PSVM model was used to predict the STK-1 groundwater level values. Meanwhile, the single PSVM model and wavelet analysis-back propagation neural network (WA-BP) model were also used for groundwater level prediction. The results show that reservoir water level fluctuation and rainfall are the main factors of groundwater level fluctuation in the reservoir landslide leading edge. We also find that the root-mean-square error (RMSE) of the proposed model for groundwater level time series prediction in STK-1 hydrology holes is 0.073m, the goodness of fit is 0.966, respectively. The prediction accuracy of WA-PSVM model is higher than the single PSVM model and WA-BP model. What is more, WA-PSVM model solves the non-linear and non-stationary problem. WA-PSVM model also has a high operating efficiency and strong applicability without considering the impacts of reservoir water level fluctuation and seasonal rainfall.
- reservoir landslide,
- groundwater level time series,
- phase-space reconstruction,
- wavelet analysis,
- particle swarm optimization,
- support vector machine,
- groundwater,
- geological hazard

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Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine Optimized by PSO Algorithm

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