黄金科学技术 ›› 2019, Vol. 27 ›› Issue (1): 137-143.doi: 10.11872/j.issn.1005-2518.2019.01.137
• • 上一篇
Xiaodan SUI,Zhouquan LUO(),Yaguang QIN,Yule WANG,Dong PENG
摘要:
为了准确预测尾矿坝浸润线的位置变化,结合浸润线埋深非稳定、非线性的时间序列以及动态变化的特点,利用小波分解与重构,提出基于小波分解的时间序列指数平滑法和BP神经网络法,采用时间序列的指数平滑法和BP神经网络方法分别对多个细节信号序列和逼近信号序列进行拟合预测,并对其拟合结果进行叠加,实现对尾矿坝浸润线的预测。将预测结果与实际监测数据进行对比,结果表明小波分解预测方法的预测结果与传统单一的指数平滑法和神经网络法预测结果相比,在预测精确度和拟合度方面:小波分解>指数平滑>神经网络。
中图分类号:
1 | 孙杨,罗黎明,邓红卫 .金属矿山深部采场稳定性分析与结构参数优化[J].黄金科学技术,2017,25(1):99-105. |
Sun Yang , Luo Liming , Deng Hongwei .Stability analysis and parameter optimization of stope in deep metal mines [J] . Gold Science and Technology, 2017,25(1):99-105. | |
2 | Hu J Y , Xie J B , Li W ,et al .Experimental research on the liquefaction potential of tailings silt in Zhuziqing tailings dam[J].Applied Mechanics and Materials,2012,(204/208):708-713. |
3 | 范拓 .尾矿库在线安全监测系统技术研究[D].广州:华南理工大学,2013. |
Fan Tuo .Research on Automatic Safety Monitoring System Technology of Mine Tailing Reservior[D].Guangzhou:South China University of Technology,2013. | |
4 | Gokmen T , Dorota S , Andrew W ,et al .Case study:Finite element method and artificial neural network models for flow through Jeziorsko earthfill dam in poland[J].Journal of Hydraulic Engineering,2005,131(6):431-440. |
5 | 谢振华,陈庆 .尾矿坝监测数据分析的RBF神经网络方法[J]. 金属矿山,2006,36(10):69-74. |
Xie Zhenhua , Chen Qing .RBF neural network method for analyzing monitoring data of tailings dam[J].Metal Mine,2006,36(10):69-74. | |
6 | Tawhid M A , Ali A F .A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function[J].Memetic Computing,2017,9(4):1-13. |
7 | 蒋卫东,李夕兵,邱更生 .基于最大Lyapunov指数分析的尾矿坝浸润线控制混沌方法[J].安全与环境学报,2003,3(5):16-20. |
Jiang Weidong , Li Xibing , Qiu Gengsheng .A new method for Chaos-controlling of seepage line in tailings dam based on analysis of largest Lyapunov exponent[J].Journal of Safety and Environment,2003,3(5):16-20. | |
8 | 宋国政,闫春明,曹佳,等 .胶东焦家成矿带超千米深部金矿勘查突破及意义——以纱岭矿区为例[J].黄金科学技术,2017,25(3):19-27. |
Song Guozheng , Yan Chunming , Cao Jia ,et al .Breakthrough and significance of exploration at depth more than 1000 m in Jiaojia metallogenic belt,Jiaodong:A case of Shaling mining area[J].Gold Science and Technology,2017,25(3):19-27. | |
9 | 邓高,杨珊 .基于组合预测与变精度粗糙模糊集的采空区稳定性评价[J].黄金科学技术,2017,25(3):98-107. |
Deng Gao , Yang Shan .Stability evaluation of goafs based on combined forecasting and variable precision rough fuzzy set [J].Gold Science and Technology,2017,25(3):98-107. | |
10 | Torrence C , Compo G P .A practical guide to wavelet analysis[J].Bulletin of the American Meteorological Society,1998,79(79):61-78. |
11 | 贺国光,马寿峰,李宇 .基于小波分解与重构的交通流短时预测法[J].系统工程理论与实践,2002,22(9):101-106. |
He Guoguang , Ma Shoufeng , Li Yu .Study on the short-term forecasting for traffic flow based on wavelet analysis[J].Systems Engineering-Theory & Practice,2002,22(9):101-106. | |
12 | 门玉明,胡高社,刘玉海 .指数平滑法及其在滑坡预报中的应用[J]. 水文地质工程地质,1997,6(1):16-18. |
Yuming Men , Hu Gaoshe , Liu Yuhai .Exponential smoothing method and its application in landslide prediction[J].Hydrogeology and Engineering Geology,1997,6(1):16-18. | |
13 | 马晓珂,王慈光 .三次指数平滑法在大秦铁路运量预测中的应用[J].华东交通大学学报,2005,22(3):8-11. |
Ma Xiaoke , Wang Ciguang .The application of the cubic exponential smoothing method on volume forecasting of Da-Qin railway[J].Journal of East China Jiaotong University,2005,22(3):8-11. | |
14 | 张德南,张心艳 .指数平滑预测法中平滑系数的确定[J].大连交通大学学报,2004,25(1):79-80. |
Zhang Denan , Zhang Xinyan .Ascertainment of index level and coefficient smooth[J].Journal of Dalian Railway Institute,2004,25(1):79-80. | |
15 | 闻新 .MATLAB神经网络应用设计[M].北京:科学出版社,2000. |
Wen Xin .Application Design of MATLAB Neural Network[M].Beijing:Science Press,2000. | |
16 | 史秀志,范玉乾,尚雪义 .基于PCA-BP神经网络模型的充填体强度预测[J].黄金科学技术,2016,24(3):64-69. |
Shi Xiuzhi , Fan Yuqian , Shang Xueyi .Strength prediction of filling body based on PCA and BP neural networks[J].Gold Science and Technology,2016,24(3):64-69. | |
17 | 郝永红,王学萌 .灰色动态模型及其在人口预测中的应用[J].数学的实践与认识,2002,32(5):813-820. |
Hao Yonghong , Wang Xuemeng .The dynamic model of gray system and its application to population forcasting [J].Mathematics in Practice and Theory,2002,32(5):813-820. | |
18 | 陈果 .神经网络模型的预测精度影响因素分析及其优化[J].模式识别与人工智能,2005,18(5):528-534. |
Chen Guo .Analysis of influence factors for forecasting precision of artificial neural network model and its optimizing[J].Pattern Recognition and Artificial Intelligence,2005,18(5):528-534. | |
19 | 范允征,林路 .线性回归模型的深度加权最小二乘估计和拟合检验[J].南京师范大学学报(自然科学版),2008,31(3):39-43. |
Fan Yunzheng , Lin Lu .Depth-Weighted LSE for linear regression model and the Fit-Test[J].Journal of Nanjing Normal University(Natural Science Edition),2008,31(3):39-43. | |
20 | 王三宝,易秀英 .基于Mathematica的灰色预测模型GM(1,1)之精度检验[J].黄石理工学院学报,2008,24(6):49-51. |
Wang Sanbao , Yi Xiuying .Accuracy test of the grey prediction model GM(1,1) based on mathematica[J].Journal of Huangshi Institute of Technology,2008,24(6):49-51. |
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