Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

WEI Bowen; XIONG Wei; LI Huokun; PENG Shengjun; XU Zhenkai

doi:10.13203/j.whugis20140570

Volume 41 Issue 9

Sep. 2016

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2016 > 41(9): 1272-1278. > DOI: 10.13203/j.whugis20140570

WEI Bowen, XIONG Wei, LI Huokun, PENG Shengjun, XU Zhenkai. Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos[J]. Geomatics and Information Science of Wuhan University, 2016, 41(9): 1272-1278. DOI: 10.13203/j.whugis20140570

Citation:

PDF (3148 KB)

Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

WEI Bowen^1,,
XIONG Wei^{1, 2},
LI Huokun^1, ,,
PENG Shengjun^{1, 2},
XU Zhenkai¹

1.
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
2.
Jiangxi Province Institute of Water Conservancy and Sciences, Nanchang 330029, China

Funds:

The National Natural Science Foundation of China 51409139

The National Natural Science Foundation of China 51569014

The National Natural Science Foundation of China 51269019

The National Natural Science Foundation of China 51469015

the Education Foundation of Jiangxi Provincial Department GJJ14223

More Information

Author Bio:
WEI Bowen, PhD, associate professor, specializes in security surveillance of hydraulic structures.bwwei@ncu.edu.cn
Corresponding author:
LI Huokun, PhD, associate professor. E-mail:lihuokun@ncu.edu.cn
Received Date: May 14, 2015
Published Date: September 04, 2016

Graphical Abstract

Abstract

Abstract

There are advantages inanalysis of theintrinsic chaotic component in the fitting residuals in displacement monitoring statistics as well as in traditional algorithms for mining dam monitoring information. This paper therefore combines the characteristics of the conventional optimization algorithm, based on using frog leaping algorithm (SFLA) to determine the optimum weight in the sub-model, to establisha dam displacement combination monitoring model based on SFLA. Taking the chaotic characteristics of fit residuals in the statistical analysis into account through using phase space reconstruction and chaos theory, we analyzed displacement residuals and predicted values, and superimposed the forecast residual term with SFLA model predictions and developed leapfrog algorithm combination forecasting methods fusing chaos residuals, and a dam displacement leapfrog algorithm implementation process that considers of the chaotic residuals. Examples show that the forecasting ability of this model provides a new, improved approach to the analysis of dam deformation.
- residuals,
- chaos theory,
- shuffled frog leaping algorithm,
- prediction model

FullText(HTML)

References (17)

References

[1]	丛培江, 顾冲时, 谷艳昌.大坝安全监控指标拟定的最大熵法[J].武汉大学学报·信息科学版, 2008, 33(11): 1 126-1 129 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200811006.htm Cong Peijiang, Gu Chongshi, Gu Yanchang. Maximum Entropy Method for Determining Dam Safety Monitoring Indices[J]. Geomatics and Information Science of Wuhan University, 2008, 33(11): 1 126-1 129 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200811006.htm
[2]	曹茂森, 邱秀梅, 夏宁.大坝安全诊断的混沌优化神经网络模型[J].岩土力学, 2006, 27(8): 1 345-1 348 http://www.cnki.com.cn/Article/CJFDTOTAL-YTLX200608024.htm Cao Maosen, Qiu Xiumei, Xia Ning. A Chaos-Optimized Neural Network Model for Dam Safety Monitoring[J]. Rock and Soil Mechanics, 2006, 27(8): 1 345-1 348 http://www.cnki.com.cn/Article/CJFDTOTAL-YTLX200608024.htm
[3]	吴中如.水工建筑物安全监控理论及其应用[M].北京:高等教育出版社, 2003 Wu Zhongru.Safety Monitoring Theory & Its Application of Hydraulic Structures[M]. Beijing: Higher Education Press, 2003
[4]	Zhang Fan, Hu Wusheng. Application of Neural Network Merging Model in Dam Deformation Analysis[J]. Journal of Southeast University(English Edition), 2013, 29(4): 441-444 http://en.cnki.com.cn/Article_en/CJFDTOTAL-DNDY201304016.htm
[5]	王新洲, 邓兴升.大坝变形预报的模糊神经网络模型[J].武汉大学学报·信息科学版, 2005, 30(7):588-591 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200507006.htm Wang Xinzhou, Deng Xingsheng. Fuzzy Neural Network Modeling for Dam Deformation Prediction[J]. Geomatics and Information Science of Wuhan University, 2005, 30(7): 588-591 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200507006.htm
[6]	何金平, 马传彬, 施玉群.高拱坝多效应量改进型D-S证据理论融合模型[J].武汉大学学报·信息科学版, 2012, 37(12):1 397-1 400 http://mall.cnki.net/magazine/article/whch201212004.htm He Jinping, Ma Chuanbin, Shi Yuqun. Multi-effect quantity Fusion Model of High Arch Dam Based on Improved D-S Evidence Theory[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12):1 397-1 400 http://mall.cnki.net/magazine/article/whch201212004.htm
[7]	赵卿, 黄声享.一种分析大坝整体位移变化规律的新方法.武汉大学学报·信息科学版, 2009, 34(12):1 419-1 422 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200912010.htm Zhao Qing, Huang Shengxiang. A Way for Overall Analysis on Dam's Displacement[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12):1 419-1 422 http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200912010.htm
[8]	金永强, 顾冲时, 于鹏.变权组合预测模型在大坝安全监测中的应用[J].水电自动化与大坝监测, 2006, 30(5): 60-62 http://www.cnki.com.cn/Article/CJFDTOTAL-DBGC200605023.htm Jin Yongqiang, Gu Chongshi, Yu Peng. Application of Combination Forecasting Model with Variable Weights to Dam Safety Monitoring[J]. Automation and Dam Monitoring, 2006, 30(5): 60-62 http://www.cnki.com.cn/Article/CJFDTOTAL-DBGC200605023.htm
[9]	孟凡生, 李美莹.组合模型在能源需求预测的应用[J].哈尔滨工业大学学报, 2013, 45(11): 106-111 http://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201311018.htm Meng Fansheng, Li Meiying. Application of Combined Model in Energy Demand Prediction[J]. Journal of Harbin Institute of Technology, 2013, 45(11): 106-111 http://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201311018.htm
[10]	Chang H, Sun W, Gu X. Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model[J]. Energies, 2013, 6(3): 1 456-1 477 doi: 10.3390/en6031456
[11]	宋晓华, 杨尚东, 刘达.基于蛙跳算法的改进支持向量机预测方法及应用[J].中南大学学报(自然科学版), 2011, 42(9): 2 737-2 740 http://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201109033.htm Song Xiaohua, Yang Shangdong, Liu Da. Improved Support Vector Machine Forecasting Model by Shuffled Frog Leaping Algorithm and Its Application[J]. Journal of Central South University (Science and Technology), 2011, 42(9): 2 738-2 740 http://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201109033.htm
[12]	Rahimi-Vahed A, Mirzaei A H. Solving a Bi-criteria Permutation Flow-Shop Problem Using Shuffled Frog-Leaping Algorithm[J]. Soft Computing, 2008, 12(5): 435-452 doi: 10.1007/s00500-007-0210-y
[13]	赵志刚, 谭云亮.基于混沌理论的煤与瓦斯突出前兆时序预测研究[J].岩土力学, 2009, 30(7): 2 186-2 189 Zhao Zhigang, Tan Yunliang. Study of Premonitory Time Series Prediction of Coal and Gas Outbursts Based on Chaos Theory[J]. Rock and Soil Mechanics, 2009, 30(7): 2 186-2 189
[14]	Eusuff M M, Lansey K E. Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm[J]. Journal of Water Resources Planning and Management, 2003, 129(3): 210-225 doi: 10.1061/(ASCE)0733-9496(2003)129:3(210)
[15]	包腾飞, 吴中如, 顾冲时.基于统计模型与混沌理论的大坝安全监测混合预测模型[J].河海大学学报(自然科学版), 2003, 31(5): 534-538 http://www.cnki.com.cn/Article/CJFDTOTAL-HHDX200305013.htm Bao Tengfei, Wu Zhongru, GU Chongshi. Statistic Model and Chaos Theory-Based Hybrid Forecasting Model for Dam Safety Monitoring[J]. Journal of Hohai University(Natural Sciences), 2003, 31(5): 534-538 http://www.cnki.com.cn/Article/CJFDTOTAL-HHDX200305013.htm
[16]	董春娇, 邵春福, 张辉, 等.基于G-P算法的快速路交通流参数相空间重构[J].吉林大学学报(工学版), 2012, 42(3): 594-599 http://cdmd.cnki.com.cn/Article/CDMD-10616-1012500769.htm Dong Chunjiao, Shao Chunfu, Zhang Hui, et al. Phase Space Reconstruction of Traffic Flow Parameters on Expressway Based on G-P Algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2012, 42(3): 594-599 http://cdmd.cnki.com.cn/Article/CDMD-10616-1012500769.htm
[17]	周小平, 刘庆义.岩石声发射混沌特征分析[J].岩土力学, 2010, 31(3): 815-820 http://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201003028.htm Zhou Xiaoping, Liu Qingyi. Research on Chaotic Characteristics of Rock Acoustic Emission [J]. Rock and Soil Mechanics, 2010, 31(3): 815-820 http://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201003028.htm

[1]	YAO Yibin, RAN Qishun, ZHANG Bao. Application of Improved Heuristic Segmentation Algorithm to Step Detection of GNSS Coordinate Time Series[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 648-654. DOI: 10.13203/j.whugis20170322
[2]	SHEN Xin, LIU Yulin, LI Shixue, YAO Huang. An Optimization Design Method for High Temporal Resolution Remote Sensing Satellite Constellation Based on Improved PSO Algorithm[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1986-1993. DOI: 10.13203/j.whugis20180160
[3]	Zhan Weiwei, Wang Wei, Chen Nengcheng, Wang Chao. Path Planning Strategies for UAV Based on Improved A*Algorithm[J]. Geomatics and Information Science of Wuhan University, 2015, 40(3): 315-320.
[4]	ZHA Feng, XU Jiangning, LI Jingshu, HE Hongyang. Improvement of a Series of Fuzzy Damp Algorithms in SINS[J]. Geomatics and Information Science of Wuhan University, 2013, 38(6): 705-709.
[5]	ZHAN Jianfeng, FAN Chong, LI Tao. A Forest Firespot Automatic Detection Algorithm for HJ-IRS Imagery[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11): 1321-1324.
[6]	LUO Zhicai, LIN Xu, ZHOU Boyang. Improved Algorithm of Autocovariance Least-Squares Noise Estimation[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1164-1167.
[7]	LI Zhenhai, LUO Zhicai, WANG Haihong, LI Qiong. Visualization of Gravity Vector Field Using Improved FLIC Algorithm[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 276-279.
[8]	YIN Shuling, SHU Ning, LIU Xinhua. Classification of Remote Sensing Image Based on Adaptive GA and Improved BP Algorithm[J]. Geomatics and Information Science of Wuhan University, 2007, 32(3): 201-204.
[9]	YAO Huanmei, HUANG Rentao, GAN Fuxing, LIU Yang. Principal Component Analysis of the Water Quality Evaluation in East Lake[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8): 732-735.
[10]	CHEN Jiangping, FU Zhongliang, XU Zhihong. An Improved Algorithm of Apriori[J]. Geomatics and Information Science of Wuhan University, 2003, 28(1): 94-99.

Cited By

Cited by

Periodical cited type(3)

1.	袁东，张健，余洋洋，张志良. 差异特征注意力引导的偏振图像高光移除. 光学技术. 2024(02): 247-256 .
2.	陈毅夫，何敬，刘刚，毛佳琪. 融合Swin-Transformer网络模型的水体高光区域提取. 遥感信息. 2023(04): 129-136 .
3.	乔玉晶，张思远，赵宇航. 高光弱纹理物体表面鲁棒重建方法. 光子学报. 2019(12): 172-182 .

Other cited types(2)

Get Citation

PDF

XML

Article views (1469) PDF downloads (269) Cited by(5)

Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

Abstract

References

Related Articles

Cited by

Periodical cited type(3)

Other cited types(2)

Catalog

Related

Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

Abstract

References

Related Articles

Cited by

Periodical cited type(3)

Other cited types(2)

Catalog

Related

Export File

Citation

Format

Content