img

QQ群聊

img

官方微信

高级检索

黄金科学技术 ›› 2022, Vol. 30 ›› Issue (4): 594-602.doi: 10.11872/j.issn.1005-2518.2022.04.164

• 采选技术与矿山管理 • 上一篇    

基于EEMD-HW-PSO-ELM耦合模型的排土场边坡位移预测模型

康恩胜1(),赵泽熙2,孟海东1   

  1. 1.内蒙古科技大学矿业与煤炭学院,内蒙古 包头 014010
    2.东北大学资源与土木工程学院,辽宁 沈阳 110819
  • 收稿日期:2021-11-08 修回日期:2022-04-03 出版日期:2022-08-31 发布日期:2022-10-31
  • 作者简介:康恩胜(1979- ),男,辽宁盘锦人,讲师,从事安全工程与矿山数据挖掘研究工作。25407924@qq.com
  • 基金资助:
    内蒙古高校基金项目“露井联采边坡失稳规律及基于多源信息失稳预测技术研究”(NJZY19127);国家级大学生创新训练计划项目“基于露井联采边坡失稳下的多源信息监测防护及研究”(202010127002)

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

Ensheng KANG1(),Zexi ZHAO2,Haidong MENG1   

  1. 1.School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia, China
    2.College of Resources and Civil Engineering, Northeastern University, Shengyang 110819, Liaoning, China
  • Received:2021-11-08 Revised:2022-04-03 Online:2022-08-31 Published:2022-10-31

摘要:

为了准确预测小样本、非线性特点的排土场边坡位移,提出了一种基于经验模态分解法、三次指数平滑法和粒子群优化极限学习机的EEMD-HW-PSO-ELM边坡位移组合预测模型。以伊敏露天矿排土场GPS位移监测数据为例,验证该模型的有效性。研究结果表明:EEMD模型分解后的边坡位移时间序列包括4个IMF分量和1个余量,将分解后的数据重构为趋势项和波动项,物理意义明确。分别选择三次指数平滑法和粒子群优化极限学习机预测趋势项和波动项位移,将分项预测结果的等权叠加值作为最终预测结果,预测值的平均相对误差为0.38%,均方根误差为1.15。选择了BP模型和Elman模型进行对比预测,结果表明组合预测模型的预测效果较好,能够为边坡安全管理提供理论依据。

关键词: 排土场, 边坡位移, 耦合模型, 集成经验模态分解, 三次指数平滑法, 粒子群优化极限学习机

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

中图分类号: 

  • TP18

图1

EEMD-HW-PSO-ELM组合模型预测流程"

图2

边坡监测布置图"

表1

排土场边坡监测数据"

监测周期变形值/mm监测周期变形值/mm
111.220120.9
220.521147.1
????
18108.837293.4
19113.538309.2

图3

GPS09监测点变形曲线"

图4

排土场边坡位移EEMD分解结果"

表2

位移波动项数据"

监测周期变形值/mm监测周期变形值/mm
1-1.36996341820-14.34116986
22.559812446214.116794032
????
18-11.451773373711.39798564
19-14.34119613817.43340374

表3

位移趋势项数据"

监测周期变形值/mm监测周期变形值/mm
112.8134411120135.2624154
218.1684032121142.9195574
????
18120.312314337281.9660058
19127.726676438291.6910052

图5

基于EEMD-HW模型的趋势项位移预测"

图6

基于EEMD-PSO-ELM模型的波动项位移预测"

表4

不同算法的排土场边坡预测精度"

模型类型MAE/%RMSE
BP模型3.9612.16
Elman模型7.6724.99
EEMD-HW-PSO-ELM模型0.381.15

图7

基于EEMD-HW-PSO-ELM模型的排土场边坡位移预测"

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.
[1] 高峰,吴晓东,周科平. 基于主成分分析和PSO-ELM算法的排土场稳定性预测模型[J]. 黄金科学技术, 2021, 29(5): 658-668.
[2] 金凌霄, 高文美. 浅析泥石流的危害及防治[J]. J4, 2005, 13(6): 45-47.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!