中文摘要：

针对岩体边坡位移预测难度大的问题,结合支持向量机-小波神经网络,提出了一种新的岩体边坡位移时序预测模型——支持向量机-小波神经网络预测模型.通过对实测位移值的学习,并借助遗传算法参数寻优的支持向量机对位移时间序列的宏观发展趋势进行滚动预测;在此基础上利用小波基函数变换分析序列的局部特征,通过2维情况下的序列局部走势方向的选择、实测值与支持向量机拟合值的相对误差与绝对误差等指标的分析,达到对预测值优化改进的目的.将该模型应用到某工程面板堆石坝坝肩强卸荷岩体边坡位移的时序预测中,结果表明,该模型具有可靠度和精度高的优点,可应用于岩体边坡位移预测分析.

英文摘要：

The accurate prediction of rock slope displacement is a difficult problem. In this paper, a new displacement time-series predicted model is proposed by combining the support vector machines and wave- let neural network, named as support vector machines-wavelet neural network. Through studying the measured displacements, parameters optimized by genetic algorithm are used to dynamically forecast the trend of time-series development. Wavelet basis function is applied to analyze the local characteristic of se- ries. By selecting the trend of the bidimensional local series and analyzing absolute error and relative error between measured values and SVM predicting values, the predicting are improved. The model was used to predict displacement time series of strong-unloading slope of the concrete faced rockfill dam. The engineer- ing case study indicates that the model is reliable and accurate; and it can be used for displacement predic- tion of the rock slopes.