大坝变形的奇异谱分析预测

利用奇异谱分析法对大坝变形数据进行分析，提取趋势和周期分量，分析影响因子与各变形分量的相关性。结果表明，大坝变形主要与水位变化和时效因子有关，温度变化对大坝变形的周期成分贡献最大，其次为水位。另外在准确提取信号的基础上,利用奇异谱分析迭代法对大坝变形进行预测，并与多元回归分析方法和高斯过程模型进行对比，发现其预测精度明显高于后两者。

Singular Spectrum Analysis for Analyzing and Forecasting the Dam Deformation

We use singular spectrum analysis to extract the corresponding components and compute the correlation coefficients, under the influence of factors. The results show that the trend term of dam deformation mainly relates to water level and aging factor. For seasonal terms, the temperature factor contributes more than water level. Experimental results of dam deformation show that SSA can extract the trends and periodic signal effectively and is useful to forecast dam deformation. Then, recurrent forecasting method of SSA is used for the prediction of dam deformation. Compared with Gaussian process and multiple-regression analysis, the results show that SSA is an effective method with a higher predictive accuracy for analyzing and forecasting dam deformation.