| 摘要: |
| 重力坝的变形与环境量之间存在复杂的非线性关系，使变形预测模型的输入自变量具有高维性，在一定程度上影响预测模型的精度和泛化能力。因此，提出一种将主成分分析、布谷鸟搜索算法和核极限学习机网络相结合的变形预测模型。该模型通过主成分分析法对与变形相关的水位、温度、时效影响因子进行主成分信息提取，优化网络模型的变量输入，同时采用优化性能更好的布谷鸟搜索算法对核极限学习机网络的核参数和正则化系数进行确定。通过某重力坝的实测资料，对坝体沿坝轴方向和上下游方向的变形位移进行预测，与多种模型进行对比，并采用不同量化指标进行评价。结果表明，本文所提模型在两个不同方向上变形预测中，确定性系数R2分别为0.943和0.931，均高于传统的神经网络和逐步回归模型；在不同测点的上下游方向变形预测中，预测的精度和模型的泛化能力均优于对比模型，从而验证了该模型的可行性和优势。 |
| 关键词: 重力坝 变形预测 主成分分析 布谷鸟搜索算法 核极限学习机 |
| DOI： |
| 分类号:TV698 |
| 基金项目:国家重点研发计划 |
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| Prediction model of gravity dam deformation based on PCA and CS-KELM |
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WANG Renchao, MA Yuming
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State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University
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| Abstract: |
| The complex nonlinear mapping relationship between the deformation of gravity dam and various environmental quantities makes the input independent variables of the deformation prediction model have high dimension, which affects the accuracy and generalization ability of the prediction model to some extent. To solve the problems, a combined prediction model is proposed, which combines principal component analysis, the cuckoo search algorithm, and the nuclear limit learning machine network. The model uses the principal component analysis method to extract the main component information of the water level, temperature, and time-dependent influencing factors related to deformation, and optimize the input of variables of the network model; furthermore, it uses the cuckoo search algorithm, which exhibits better optimization performance than , to determine the kernel parameters and regularization coefficients of the kernel extreme learning machine network. With the measured data of a gravity dam, the deformation displacement of the dam in the direction of the dam axis and the upstream and downstream directions are predicted, compared with those of various models, and evaluated with different quantitative indicators. The analysis results reveal that the certainty coefficients R2, of the proposed model in the two different directions are 0.943 and 0.931, respectively, which are higher than those of the traditional neural network model and the stepwise regression model. In the upstream and downstream direction deformation predictions of different measurement points, the accuracy and generalization ability of the model are better than those of the comparison model, thus verifying the feasibility and advantages of the model. |
| Key words: gravity dam deformation prediction principal component analysis cuckoo search algorithm kernel extreme learning machine |
