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Gold Science and Technology ›› 2019, Vol. 27 ›› Issue (1): 137-143.doi: 10.11872/j.issn.1005-2518.2019.01.137

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Study on Prediction Method of Seepage Line of Tailings Dam Based on Wavelet Decomposition

Xiaodan SUI,Zhouquan LUO(),Yaguang QIN,Yule WANG,Dong PENG   

  1. 1. School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China
  • Received:2017-08-18 Revised:2017-11-30 Online:2019-02-28 Published:2019-03-19
  • Contact: Zhouquan LUO E-mail:lzq505@hotmail.com

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Abstract:

The seepage line is the safe lifeline of tailings dam, and its location change directly reflects the seepage characteristics inside the dam body.In order to accurately predict the location change of the seepage line of tailings dam in flood season, analyze the law of change,and predict the future change of seepage line,so as to ensure production, reduce accidents such as dam break, and ensure the safety of people’s life and property. Combined with the characteristics of unstable, nonlinear time series and dynamic change of the submerged line of the tailings dam, using wavelet decomposition and reconstruction, the exponential smoothing analysis method of time series based on wavelet decomposition and the analysis method of BP neural network are proposed. Firstly, the non-stationary time series s is decomposed into five detail signal sequences and one approximate signal sequence by wavelet decomposition. The d1~d5 of the detailed signal sequence is predicted by exponential smoothing method of time series. The BP neural network method is used to predict the approximate signal sequence A5. Finally, wavelet reconstruction is used to predict the approximate signal sequence A5 based on MATLAB programming, and the fitting results are superimposed to predict the soakage line of the tailings dam. In this paper, a metal tailings dam with more precipitation and humid climate is selected as the research object, and the data of the buried depth of the infiltration line in the flood season for 100 days are selected for modeling and analysis. The time series exponential smoothing analysis method based on wavelet decomposition and the BP neural network analysis method are used to predict the development trend of the buried depth of the infiltrating line in the next 10 days, and the predicted results are compared with the actual monitoring data.The results show that the prediction results of wavelet decomposition method are compared with those of traditional single exponential smoothing and neural network prediction methods. The prediction accuracy is as follows: wavelet decomposition (0.9287) > exponential smoothing (0.9038) > neural network (0.8725). The prediction error of wavelet decomposition is the smallest and the accuracy is the highest. In terms of fitting degree, wavelet decomposition (0.8837) > exponential smoothing (0.8573) > neural network (0.8462). The fitting result of wave decomposition prediction method is almost consistent with the whole development trend of original time series, and the degree of coincidence is high. Therefore, This method has good applicability and superiority in predicting the buried depth of tailing dam in flood season.

Key words: tailings dam, phreatic line, wavelet decomposition, exponential smoothing, BP neural network, prediction

CLC Number: 

  • TD76

Fig.1

General prediction steps"

Fig.2

Structure diagram of feed-forward neural network"

Fig.3

Wavelet decomposition prediction steps"

Fig.4

Real map of a tailings"

Table 1

Data of 100 days before the penetration depth of tailings dam（m）"

序号 埋深 序号 埋深 序号 埋深 序号 埋深
1 13.85 11 13.55 … 81 13.78 91 13.76
2 13.92 12 13.64 82 13.71 92 13.67
3 13.80 13 13.77 83 13.65 93 13.55
4 13.89 14 13.84 84 13.62 94 13.51
5 13.80 15 13.75 85 13.55 95 13.48
6 13.75 16 13.71 86 13.71 96 13.54
7 13.67 17 13.65 87 13.75 97 13.60
8 13.54 18 13.59 88 13.80 98 13.69
9 13.50 19 13.54 89 13.70 99 13.74
10 13.43 20 13.65 90 13.67 100 13.78

Fig.5

Results after wavelet db3 decomposition"

Fig.6

Comparison of prediction results of different methods with measured results"

Fig.7

Comparison of fitting results of different forecasting methods with original time series"

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