基于EEMD-HW-PSO-ELM耦合模型的排土场边坡位移预测模型

doi:10.11872/j.issn.1005-2518.2022.04.164

Abstract

Abstract:

In order to accurately predict the displacement of waste dump slope with small samples and nonlinear characteristics，an EEMD-HW-PSO-ELM slope displacement combined prediction model based on empirical mode decomposition method，cubic exponential smoothing method and particle swarm optimization limit learning machine was proposed.Taking the GPS displacement monitoring data of Yiminhe open pit waste dump as an example to verify the effectiveness of the model，the research results show that the time series of slope displacement decomposed by EEMD model includes four IMF components and one margin.The decomposed data is reconstructed into trend term and fluctuation term with clear physical meaning.The cubic exponential smoothing method and particle swarm optimization limit learning machine were selected to predict the displacement of trend term and fluctuation term respectively.The equal weight superposition value is the final prediction result.The average relative error of the prediction value is 0.38% and the root mean square error is 1.15.BP model and Elman model were selected for comparative prediction.The results show that the com-bined prediction model has good prediction effect and can provide a theoretical basis for safety management.

Key words: dump, slope displacement, coupling model, integrated empirical mode decomposition, cubic exponential smoothing method, particle swarm optimization extreme learning machine

CLC Number:

TP18

Ensheng KANG,Zexi ZHAO,Haidong MENG. Displacement Prediction of Dump Slope Based on EEMD-HW-PSO-ELM Coupling Model[J].Gold Science and Technology, 2022, 30(4): 594-602.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Dump slope monitoring data"

监测周期	变形值/mm	监测周期	变形值/mm
1	11.2	20	120.9
2	20.5	21	147.1
$?$	$?$	$?$	$?$
18	108.8	37	293.4
19	113.5	38	309.2

Table 1

Fig.3

Fig.4

Table 2

Displacement fluctuation term data"

监测周期	变形值/mm	监测周期	变形值/mm
1	-1.369963418	20	-14.34116986
2	2.559812446	21	4.116794032
$?$	$?$	$?$	$?$
18	-11.45177337	37	11.39798564
19	-14.3411961	38	17.43340374

Table 2

Table 3

Displacement trend item data"

监测周期	变形值/mm	监测周期	变形值/mm
1	12.81344111	20	135.2624154
2	18.16840321	21	142.9195574
$?$	$?$	$?$	$?$
18	120.3123143	37	281.9660058
19	127.7266764	38	291.6910052

Table 3

Fig.5

Fig.6

Table 4

Fig.7

References 0

	Cao Lanzhu， Wang Zhen， Wang Dong，et al，2017.Research on theory and method of landslide early warning in open-pit mine［J］.China Safety Science Journal，27（3）：163-168.
	Deng Dongmei， Liang Ye， Wang Liangqing，et al，2017.Displacement prediction method based on ensemble empirical mode decomposition and support vector machine regression—A case of landslides in Three Gorges Reservoir area ［J］.Rock and Soil Mechanics，38（12）：3660-3669.
	Du Juan， Yin Kunlong， Chai Bo，2009.Study of displacement prediction model of landslide based on response analysis of inducing factors ［J］.Chinese Journal of Rock Mechanics and Engineering，28（9）：1783-1789.
	Duan Gonghao， Niu Ruiqing， Zhao Yannan，et al，2016.Rainfall induced landslide prediction based on dynamic exponential smoothing model ［J］.Geomatics and Information Science of Wuhan University，41（7）：958-962.
	Duan W Y， Han Y， Huang L M，et al，2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height［J］.Ocean Engineering，124：54-73．
	Guo Zizheng， Yin Kunlong， Huang Faming，et al，2018.Landslide displacement prediction based on surface monitoring data and nonlinear time series combination model ［J］ .Chinese Journal of Rock Mechanics and Engineering，37（Supp.1）：3392-3399.
	Han Yongliang， Li Sheng， Yang Hongwei，et al，2015.Ｒesearch on LMD-BA-ELM-based model for nonlinear prediction of slope deformation time-series ［J］.China Safety Science Journal，25（9）：59-65.
	Huang G B， Zhu Q Y， Siew C K，2006.Extreme learning machine：Theory and applications ［J］ .Neurocomputing，70（1/2/3）：489-501.
	Huang N E， Shen Z， Long S R，et al，1998.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis［J］.Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences，454（1971）：903-995..
	Li Bin， Li Yibin，2011.Chaotic time series prediction based on ELM learning algorithm ［J］.Journal of Tianjin University，44（8）：701-704.
	Li Huajin， Xu Qiang， He Yusen，et al，2016.Predictive model of landslide displacement by wavelet analysis and multiple extreme learning machines ［J］.Journal of Engineering Geology，24（5）：721-731.
	Li Limin， Guo Fu， Wen Zongzhou，et al，2020.Dynamic prediction of landslide displacement based on long short time memory and multiple influencing factors［J］.Science Technology and Engineering，20（33）：13559-13567.
	Li Runqiu， Shi Shiliang， Wu Aiyou，et al，2014.Research on coal mining workface gas emission prediction method based on EMD-Elman and its application ［J］.China Safety Science Journal，24（6）：51-56.
	Lian C， Zeng Z G， Yao W，et al，2014.Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis［J］.Neural Computing and Applications，24（1）：99-107.
	Peng Ling， Niu Ruiqing， Wu Ting，2013.Time series analysis and support vector machine for landslide displacement prediction［J］.Journal of Zhejiang University（Engineering Science），47（9）：1672-1679.
	Shi Peiming， Ding Xuejuan， Li Geng，et al，2013.An improved method of EMD and its applications in rotating machinery fault diagnosis［J］.Journal of Vibration and Shock，32（4）：185-190．
	Sun Shiguo， Wang Chao， Zhao Juan，2017.Displacement prediction based on optimized gray model of dump slope of Zijinshan ［J］.Journal of Henan University of Urban Construction，26（4）：1-6.
	Tan Mengjiao， Yin Kunlong， Guo Zizheng，et al，2019.Landslide displacement prediction based on CEEMDAN method and Particle Swarm Optimized-Extreme Learning Machine model［J］.Geological Science and Technology Information，38（6）：165-175.
	Xie Bo， Shi Fuqiang， Liao Xueyan，et al，2020.Slope displacement prediction method based on EEMD-PSO-ELM model［J］.China Safety Science Journal，30（3）：157-162．
	Yang A C， Tsai S， Huang N E，2011.Decomposing the association of completed suicide with air pollution，weather and unemployment data at different time scales［J］.Journal of Affective Disorders，129（1/2/3）：275-281.
	Zhang G F， Liu H M， Dong Z L，2015.Efficient non-consecutive feature tracking for robust structure from motion［ J］.IEEE Trans Image Process，25（12）：5957-5970.
	Zhang Kaixiang， Niu Ruiqing， Hu Youjian，et al，2017.Landslide displacement prediction based on wavelet transform and external response ［J］.Journal of China University of Mining and Technology，46（4）：924-931.
	Zhu Sainan， Yin Yueping， Li Bin，2018.Evolution characteristics of weak intercalation in massive layered rockslides－A case study of Jiweishan rockslide in Wulong，Chong-qing［J］.Journal of Engineering Geology，26（6）：1638-1647．
	Zhu X， Xu Q， Tang M G，et al，2017.A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides［J］.Neural Computing＆Applications：1-11. DOI：10.1007/s00521-017-2968-x . doi: 10.1007/s00521-017-2968-x
	曹兰柱，王珍，王东，等，2017.露天矿滑坡预警理论与方法研究［J］.中国安全科学学报，27（3）：163-168.
	邓冬梅，梁烨，王亮清，等，2017.基于集合经验模态分解与支持向量机回归的位移预测方法：以三峡库区滑坡为例［J］.岩土力学，38（12）：3660-3669．
	杜娟，殷坤龙，柴波，2009.基于诱发因素响应分析的滑坡位移预测模型研究［J］.岩石力学与工程学报，28（9）：1783-1789.
	段功豪，牛瑞卿，赵艳南，等，2016.基于动态指数平滑模型的降雨诱发型滑坡预测［J］.武汉大学学报（信息科学版），41（7）：958-962.
	郭子正，殷坤龙，黄发明，等，2018.基于地表监测数据和非线性时间序列组合模型的滑坡位移预测［J］.岩石力学与工程学报，37（增1）：3392-3399.
	韩永亮，李胜，杨宏伟，等，2015.基于LMD-BA-ELM的边坡变形时序非线性预测模型研究［J］.中国安全科学学报，25（9）：59-65.
	李彬，李贻斌，2011.基于 ELM 学习算法的混沌时间序列预测［J］.天津大学学报，44（8）：701-704.
	李骅锦，许强，何雨森，等，2016.WA联合ELM与OS-ELM的滑坡位移预测模型［J］.工程地质学报，24（5）：721-731.
	李丽敏，郭伏，温宗周，等，2020.基于长短时记忆与多影响因子的滑坡位移动态预测［J］.科学技术与工程，20（33）：13559-13567.
	李润求，施式亮，伍爱友，等，2014.采煤工作面瓦斯涌出预测的EMD-Elman方法及应用［J］.中国安全科学学报，24（6）：51-56.
	彭令，牛瑞卿，吴婷，2013.时间序列分析与支持向量机的滑坡位移预测［J］.浙江大学学报（工学版），47（9）：1672-1679.
	时培明，丁雪娟，李庚，等，2013.一种EMD改进方法及其在旋转机械故障诊断中的应用［J］.振动与冲击， 32（4）：185-190．
	孙世国，王超，赵娟，2017.基于优化灰色模型的紫金山排土场边坡位移预测研究［J］.河南城建学院学报，26（4）：1-6.
	檀梦皎，殷坤龙，郭子正，等，2019.基于CEEMDAN理论和PSO-ELM模型的滑坡位移预测［J］.地质科技情报，38（6）：165-175.
	谢博，施富强，廖学燕，等，2020.边坡位移的EEMD-PSO-ELM模型预测方法［J］.中国安全科学学报，30（3）：157-162.
	张凯翔，牛瑞卿，胡友健，等，2017.基于小波变换及外因响应的滑坡位移预测［J］.中国矿业大学学报，46（4）：924-931.
	朱赛楠，殷跃平，李滨，2018.大型层状基岩滑坡软弱夹层演化特征研究——以重庆武隆鸡尾山滑坡为例［J］.工程地质学报，26（6）：1638-1647.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

模型类型	MAE/%	RMSE
BP模型	3.96	12.16
Elman模型	7.67	24.99
EEMD-HW-PSO-ELM模型	0.38	1.15

[1]	Keping ZHOU,Lixiong CAO,Jielin LI,Wei ZHANG,Chengye YANG,Xiaoping ZHANG,Le GAO. Stability Numerical Analysis and Safety Classification Evaluation of Complex Goaf Group [J]. Gold Science and Technology, 2022, 30(3): 324-332.
[2]	Feng GAO,Xiaodong WU,Keping ZHOU. Prediction Model of Soil Dump Stability Based on Principal Component Analysis and PSO-ELM Algorithm [J]. Gold Science and Technology, 2021, 29(5): 658-668.
[3]	Yongchun LIU,Liguan WANG,Jiaxi WU. Optimization of Control Parameters for Underground Load-Haul-Dump Machine Based on LQR-QPSO [J]. Gold Science and Technology, 2021, 29(1): 25-34.
[4]	LAN Bibo，LI Tingli，CHEN Shuping，WANG Jun’e，WU Zengling，WANG Chun，FAN Jingbiao， ZHANG Huanran，ZHANG Peng. Research on the Copper Reactions of Copper-bearing Gold Ore in the Dump Leaching Process [J]. Gold Science and Technology, 2013, 21(5): 136-139.
[5]	CHI Jisong，GUO Wei，WANG Yuewei，XING Yingying. Discussion on the Ways of Tailing Dumps and Drainage of Penglai Mining Concentrator,Shandong Province [J]. J4, 2011, 19(6): 72-74.
[6]	JIN Lingxiao,GAO Wenmei. Analysis on Damage of Mud-rock Flow and its Preventive Measures [J]. J4, 2005, 13(6): 45-47.
[7]	ZHANG An-Ji, FANG Zhao-Heng. The evolution and current situation of biooxidation of sulfide minerals [J]. J4, 2000, 8(6): 28-35.
[8]	WANG Xu-Dong. Technologically flowchart and production practice of making grains and leaching in dumps of clayey oxide [J]. J4, 2000, 8(1): 32-35.
[9]	Pei Hongming. PLANT PRACTICE OF TINY GOLD MINE [J]. J4, 1998, 6(5-6): 48-49.

Displacement Prediction of Dump Slope Based on EEMD-HW-PSO-ELM Coupling Model

RichHTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 11

References 0

Related Articles 9

Metrics

Comments

Recommended 0