Mine Slope Displacement Prediction Based on ICEEMDAN and Attention-LSTM

doi:10.12068/j.issn.1005-3026.2025.20240197

Abstract

Abstract:

In order to improve the accuracy of mine slope displacement prediction， a mine slope displacement prediction model based on an improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）， least squares fitting method and long short-term memory network integrated into the attention mechanism was proposed. Firstly， considering the temporal and nonlinear characteristics of the mining slope displacement monitoring data， an improved mode decomposition method was employed to perform the time-frequency decomposition of cumulative displacement， resulting in trend， periodic， and random components， thereby effectively reducing the data complexity. Secondly， to predict the trend component， a cubic polynomial regression prediction model was developed by using the least squares fitting method. To predict the periodic component， an attention mechanism was introduced to distinguish the importance of displacement data at different times， which effectively captured the internal dependencies within the long-term displacement sequences. Finally， the predicted results of the trend and periodic components were integrated to obtain the cumulative displacement of the mining slope. Taking the slope of Julong Copper Mine in Xizang as an example， the performance of the proposed method was tested. The results demonstrate that the proposed slope displacement prediction model achieves a root mean square error （RMSE） of 5.99 mm and a mean absolute percentage error （MAPE） of 5.94%. The RMSE and MAPE decrease by 51.30% and 55.17% compared with the traditional LSTM model， respectively. These results highlight the significant improvement in prediction accuracy achieved by the proposed method.

Key words: displacement prediction, open-pit mining, landslide, long short-term memory（LSTM）network, attention mechanism

CLC Number:

TD 804

Hui LI, Xiao-fei HAN, Wan-cheng ZHU, Jia-shi MAO. Mine Slope Displacement Prediction Based on ICEEMDAN and Attention-LSTM[J]. Journal of Northeastern University(Natural Science), 2025, 46(7): 163-170.

Figures/Tables 9

Fig.1 Structure diagram of the long short-term memory network

Fig.2 Structure diagram of the attention mechanism

Fig.3 Structure diagram of Attention-LSTM

Fig.4 Flowchart of cumulative displacement prediction

Fig.5 Cumulative displacement of monitoring points and decomposition of cumulative displacement

Fig.6 Prediction of trend displacement

Fig.7 Prediction of periodic displacement

Fig.8 Prediction of cumulative displacement

Table 1 Prediction results of slope cumulative

指标	LSTM	ICEEMDAN-CPF-LSTM	ICEEMDAN-CPF-Attention-LSTM
$e M A P E$ /%	13.250	7.820	5.940
$e R M S E$ /mm	12.300	7.990	5.990
$R 2$	0.800	0.905	0.964

Table 1 Prediction results of slope cumulative

指标	LSTM	ICEEMDAN-CPF-LSTM	ICEEMDAN-CPF-Attention-LSTM
$e M A P E$ /%	13.250	7.820	5.940
$e R M S E$ /mm	12.300	7.990	5.990
$R 2$	0.800	0.905	0.964

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