坝肩边坡变形在外部因素影响下呈现出不确定性和随机性,从而不易预测。基于聚类模态分解(EEMD)、样本熵(SE)和改进型粒子群算法优化的最小二乘支持向量机(IPSO-LSSVM)方法,提出一种名为EEMD-SE-IPSO-LSSVM的坝肩边坡变形预测模型。首先,利用EEMD将原始坝肩边坡变形时间序列分解为若干个不同复杂度的子序列,并基于SE判定各子序列的复杂度,将相近的子序列进行合并重组以减少计算规模;然后,分别对各重组子序列建立IPSO-LSSVM预测模型;最后,将各预测分量进行叠加重构,得到最终的大坝变形预测值。以澜沧江苗尾水电站左岸坝肩边坡为例,将BPNN、RBFNN、LSSVM、EEMD-SE-LSSVM与EEMD-SE-PSO-LSSVM进行对比研究。结果表明,该模型的计算精度优于其他神经网络模型,具有较好的适宜性和稳定性,是一种可靠的坝肩边坡变形预测方法,能够为大坝安全监测提供有价值的参考。
Abstract
The deformation of abutment slope is difficult to be estimated as it is uncertain and random under the influence of many factors. We present a prediction model for the abutment slope deformation by coupling ensemble empirical mode decomposition (EEMD), sample entropy (SE), improved particle swarm optimization (IPSO), and least square support vector machine (LSSVM). First of all, the time series of abutment slope deformation is decomposed by the EEMD into several subsequences with different complexity. Secondly, the complexity of each subsequence is determined using SE, and similar subsequences are merged to reduce the computational scale. Subsequently, the prediction model based on IPSO-LSSVM is established for each newly merged subsequence. The final prediction value of abutment slope deformation is obtained through superimposing and reconstructing each component. The model is applied to predicting the abutment slope deformation on the left bank of Miaowei Hydropower Station on Lancang River as a case study, and the result is compared with those of BPNN, RBFNN, LSSVM, EEMD-SE-LSSVM and EEMD-SE-PSO-LSSVM models. The comparison demonstrates that the present model has higher accuracy and better stability than the other neural network models.
关键词
坝肩边坡 /
变形预测 /
聚类经验模态分解 /
样本熵 /
改进型粒子群算法 /
最小二乘支持向量机
Key words
abutment slope /
deformation prediction /
ensemble empirical mode decomposition (EEMD) /
sample entropy (SE) /
improved particle swarm optimization (IPSO) /
LSSVM
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基金
国家创新研究群体科学基金项目(41521002);高等学校博士学科点专项科研基金项目(20135122130002)
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