%0 Journal Article %T 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<br>Landslide Groundwater Level Time Series Prediction Based on Phase Space Reconstruction and Wavelet Analysis-Support Vector Machine 〇ptimized by PS〇 Algorithm %A 黄发明 %A 殷坤龙 %A 张桂荣 %A 周春梅 %A 张 俊< %A br> %A Huang Faming' %A Yin Kunong· %A Zhang Guirong %A Zhou Chunmei %A Zhang Jun %J 地球科学(中国地质大学学报) %D 2015 %R 10.3799/dqkx.2015.105 %X 预测滑坡地下水位的动态演变过程对滑坡稳定性分析具有重要意义,三峡库区库岸滑坡地下水位时间序列受多种因素影 响,呈现出高度非线性非平稳的特征.为对其进行预测,提出一种基于相空间重构的小波分析-粒子群优化支持向量机(wavelet analysis-supportvectormachine,简称WA-PSVM)模型.该模型引入小波变换法对地下水位序列进行时频分解,将非平稳的地下水 位序列转变为多个不同分辨率尺度下的较平稳的地下水位子序列;然后重构各子序列的相空间,再利用PSVM(全称supportvectormachine) 模型对地下水位各子序列进行预测,最后将各子序列预测值相加得到最终预测结果.以三峡库区三舟溪滑坡前缘 STK-1水文孔日平均地下水位序列为例,首先分析滑坡前缘地下水位变化的影响因素,再将WA-PSVM 模型应用于地下水位预 测,并与单独PSVM 模型和小波分析-BP网络模型(waveletanalysis-backpropagation,简称WA-BP)作对比.结果表明:滑坡前缘地 下水位受降雨和库水位影响较大,利用WA-PSVM 模型对STK-1水文孔地下水位进行预测的均方根误差为0.073m、拟合优度 为0.966,WA-PSVM 模型预测精度高于单独PSVM 模型和WA-BP模型.WA-PSVM 模型解决了地下水位序列非线性非平稳的 问题,在不考虑影响因素的情况下能获得满意的预测效果,具有较高的建模效率和较强的实用性.<br>It is of great significance to predict the dynamic evolution process of landslide underground waterleve1 forlandslide stability analysis. For the problem that the evolution process of groundwaterleve1 in reservoirlandslide is a highly non-1inear and non-stationary time series affected by many factors, to predictlandslide groundwaterleve1 time series, a coupling mode1 based on phase space reconstruction and wavelet analysis-support vector machine(WA-PSVM) optimized by particle swarm optimization is proposed. Firstly, the groundwaterleve1 time series was decomposed into severa1 different frequency compo- nents to transform the non-stationary groundwaterleve1 time series into stationary time series. Secondly, the PSVM mode1 was established for each component prediction based on the phase-space reconstruction. Atlast, the fina1 prediction result was ob- tained by adding the predicted values of a11 frequency components. Taking daily average groundwaterleve1 time series of STK- 1 hydro1ogy hole on Sanzhouxi Landslide in the Three Gorges Reservoir Area for example, the influencing factors of landslide groundwaterleve1 lluctuation were analyzed and WA-PSVM mode1 was used to predict the STK-1 groundwaterleve1 values. Meanwhile, the single PSVM mode1 and wavelet analysis-back propagation neura1 network(WA-BP) mode1 were also used for groundwaterleve1 prediction. The results show that reservoir waterleve1 fluctuation and rainfa11 are the main factors of ground- waterleve1 fluctuation in the reservoirlandslideleading edge. We also find that the root-mean-square error(RMSE) of the pro- posed mode1 for groundwaterleve1 time series prediction in STK-1 hydro1ogy holes is 0.073 m, the goodness of fit is 0.966, re- spectively. The prediction accuracy of WA-PSVM mode1 is higher than the single PSVM mode1 and WA-BP mode1. What is more, WA-PSVM modelsolves the non-1inear and non-stationary problem. WA-PSVM mode1 also has a high operating effi- %K 库岸滑坡 %K 地下水位时间序列 %K 相空间重构 %K 小波分析 %K 粒子群算法 %K 支持向量机 %K 地下水 %K 地质灾害.< %K br> %K reservoirlandslide %K groundwaterleve1 time series %K phase-space reconstruction %K wavelet analysis %K particle swarm optimization %K support vector machine %K groundwater %K geo1ogica1 hazard. %U http://www.earth-science.net/WebPage/Article.aspx?id=3113