大坝变形的小波分析与ARMA预测模型

大坝变形观测资料可视为非平稳时间序列，从影响大坝变形规律的因素出发，可将其分解为主值函数项、周期函数项和改进后的平稳时间序列。其中主值函数项采用逐步回归法拟合，针对时效因子采用半经验公式无法准确拟合实际变化情况，采用小波分析法将序列分解为低频和高频两部分信号，其中低频部分代表时效等因素影响的变形趋势；高频部分代表水位、温度等影响的变化规律，应用时间序列原理分别建立变形预测ARMA（p，q）模型，从而在现有水位、温度观测资料下预测坝体未来的变形趋势。实例计算结果表明，结合小波分析的时间序列法建立的预测模型，预测精度高于统计回归分析，预测效果良好，可作为一种有效方法应用于大坝变形预测中。

Wavelet analysis and ARMA prediction model for dam deformation

Dam deformation observation data can be regarded as non-stationary time series, and considering the influence factors of dam deformation, it can be decomposed into principal value function terms, periodic function terms and improved stationary time series. As the semi-empirical formula of time effect factors can not accurately fit the actual changes, a wavelet analysis is made to decompose the series into low frequency and high frequency signals: the low frequency signal represents time effect deformation trend while the high frequency signal represents the changes in water level and temperature. Then the deformation prediction ARMA model can be established by the time series analysis. The tendency of dam deformation can be predicted based on the water level and temperature observation data in the future. The actual calculated results show that the prediction reliability which is established by the time series analysis method is better than that by the regression analysis model; and the wavelet analysis method can be used as an effective method for dam deformation prediction.