Prediction of Dam Deformation Monitoring Data Based on EEMD-GA-BP Model

YAN Hong-bo; ZHOU Bin; LU Xian-jian; LIU Hai-feng

doi:10.11988/ckyyb.20180160

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Journal of Changjiang River Scientific Research Institute ›› 2019, Vol. 36 ›› Issue (9) : 58-63. DOI: 10.11988/ckyyb.20180160

ENGINEERING SAFETY AND DISASTER PREVENTION

Prediction of Dam Deformation Monitoring Data Based on EEMD-GA-BP Model

YAN Hong-bo^1,2, ZHOU Bin¹, LU Xian-jian^1,2, LIU Hai-feng¹

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Abstract

A prediction model of dam deformation monitoring data integrating Ensemble Empirical Mode Decomposition (EEMD), Genetic Algorithm (GA) and Back Propagation (BP) neural network is built to tackle the unstable performance and the drift of measured value of automatic monitoring data of dam deformation. The EEMD is used to extract the low-frequency signals which reflect the true deformation of dam and to remove the noise and outliers in the data of the automatic monitoring system; the GA-optimized BP neural network is employed to learn and extrapolate the real signals. The model-predicted deformation values are compared with measured values and also predicted values of some other methods in terms of residual error. Case study demonstrates that the proposed model could improve the prediction accuracy of dam deformation effectively.

Key words

dam deformation / prediction model / EEMD / BP neural network / genetic algorithm

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YAN Hong-bo, ZHOU Bin, LU Xian-jian, LIU Hai-feng. Prediction of Dam Deformation Monitoring Data Based on EEMD-GA-BP Model[J]. Journal of Changjiang River Scientific Research Institute. 2019, 36(9): 58-63 https://doi.org/10.11988/ckyyb.20180160

References

[1] 张昊宇, 周克勤, 宋亚腾, 等. 基于新型 FMCW 地基合成孔径雷达的大坝变形监测. 长江科学院院报, 2017, 34(12): 33-37.
[2] 吴云芳,李珍照.大坝神经网络预报模型与大坝回归预报模型的比较.长江科学院院报,2003,20(2):50-53.
[3] 姜振翔, 徐镇凯, 魏博文. 基于小波分解和支持向量机的大坝位移监控模型. 长江科学院院报, 2016, 33(1): 43-47.
[4] 王利, 张勤, 李亚红. 基于中值滤波的灰色预测模型及其在大坝变形预测中的应用. 测绘科学, 2007, 32(2): 135-137.
[5] 徐锋, 王崇倡, 张飞. 粒子群-BP 神经网络模型在大坝变形监测中的应用. 测绘科学, 2012, 37(4): 181-183.
[6] HUANG N E, SHEN Z, LONG S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Non-linear and Non-stationary Time Series Analysis. Proceedings of the Royal Society A, 1998, 454(1971): 903-995.
[7] 王贺, 胡志坚, 陈珍, 等. 基于集合经验模态分解和小波神经网络的短期风功率组合预测. 电工技术学报, 2013, 28(9): 137-144.
[8] 王小平, 曹立明. 遗传算法:理论应用及软件实现. 西安:西安交通大学出版社, 2002.
[9] SRINIVAS M, PATNAIK L. Learning Neural Network Weights Using Genetic Algorithms-Improving Performance by Search-space Reduction∥Proceedings of the IEEE International Joint Conference on Neural Networks. Singapore, November 18-21, 1991: 2331-2336.
[10] SCHAFFER J D, CARUANAR, ESHELMAN L J, et al. A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization∥Proceedings of the Third International Conference on Genetic Algorithms. San Francisco, June 4-7, 1989: 51-60.
[11] SCHAFFER J D. Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms. Nashville, TN: Vanderbilt University, 1985.
[12] 李潇, 徐进军. 动态稳健支持向量机在大坝变形预测中的应用. 大地测量与地球动力学, 2009, 29(2): 118-120.
[13] 李春宇, 张晓林, 张展, 等. 基于 DFT 的正弦波初相估计算法及误差分析. 北京航空航天大学学报, 2007, 33(5): 580-584.