基于经验模态分解和遗传支持向量机的多尺度大坝变形预测

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摘要利用经验模态分解算法分解大坝变形数据，得到不同物理特征尺度的变形分量，分析各变形分量特征及其相关影响因素。针对各变形分量的特点，分别建立基于GA-SVM的各变形分量预测模型，将各分量预测模型相加，最终构建基于经验模态分解和支持向量机的多尺度变形预测模型。由大坝变形数据的经验模态分解实例分析，证实经验模态分解算法能有效对大坝变形数据进行多尺度分解，由经验模态分解算法分解得到的各变形分量其物理特征更加显著，更易于各变形分量影响因素分析和变形模型建立，因此，针对各变形分量的特点所建立的GA-SVM的各变形分量模型具有较高精度。基于经验模态分解和支持向量机的多尺度变形预测模型由各分量预测模型相加而得，能充分挖掘大坝变形中隐含的多种内在规律，能同时在不同特征尺度上进行大坝变形预测。通过对多尺度大坝变形预测模型和多元回归、时间序列分析、GM(1，4)、BP网络和GA-SVM大坝变形预测模型进行精度对比，证实基于经验模态分解和支持向量机的多尺度变形预测模型是一种精度较高的大坝变形预测新方法。

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关键词 ：水利工程, 经验模态分解, 支持向量机, 遗传算法, 多尺度, 变形预测

Abstract：Empirical mode decomposition algorithm is used to decompose dam deformation data. By obtaining the multi-scale deformation components，the characteristics of deformation components and their related influence factors are analyzed. According to those characteristics，the independent GA-SVM deformation component prediction models are built，used to combine construct the empirical mode decomposition based GA-SVM multi-scale deformation prediction model. By analyzing the dam deformation data from a real case with the empirical mode decomposition，the following views is proved: the empirical mode decomposition can effectively perform the multi-scale decomposition on dam deformation data；and the deformation components decomposed by empirical mode decomposition algorithm have more clear characteristics which can be easily applied to the influence factors analysis for deformation components and surely achieve an higher prediction accuracy. Since the empirical mode decomposition based on GA-SVM multi-scale deformation prediction model is composed of different deformation components of prediction models，it is capable of showing the intrinsic principles of dam deformation，and able to perform dam deformation prediction in different feature scales simultaneously. Comparing accuracies via multi-scale dam deformation prediction model and multiple regression，time series analysis，GM(1，4)，BP neural network and GA-SVM dam deformation prediction model，the empirical mode decomposition based GA-SVM multi-scale deformation prediction model is proved as a valuable new method with higher accuracy for dam deformation prediction.

Key words： hydraulic engineering empirical mode decomposition support vector machines(SVM) genetic algorithm multi-scale deformation prediction

收稿日期: 2010-06-25

引用本文:

张豪，许四法. 基于经验模态分解和遗传支持向量机的多尺度大坝变形预测[J]. 岩石力学与工程学报, 2011, 30(S2): 3681-3688.
ZHANG Hao，XU Sifa. MULTI-SCALE DAM DEFORMATION PREDICTION BASED ON EMPIRICAL MODE DECOMPOSITION AND GENETIC ALGORITHM FOR SUPPORT VECTOR MACHINES (GA-SVM). , 2011, 30(S2): 3681-3688.

链接本文:

http://www.rockmech.org/CN/Y2011/V30/IS2/3681

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