Prediction of mine water inflow along mining faces using the SSA-CG-Attention multifactor model

Authors

DING Yingying, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, ChinaFollow
YIN Shangxian, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, ChinaFollow
LIAN Huiqing, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
LIU Wei, College of Construction Engineering, Jilin University, Changchun 130026, China
LI Qixing, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
QI Rongrong, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
BU Changsen, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
XIA Xiangxue, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
LI Shuqian, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China

Abstract

Predicting mine water inflow plays an important role in ensuring mine safety, optimizing resource allocation, and improving work efficiency. This study aims to improve the accuracy and stability of the predicted mine water inflow. Given their strong correlations with water inflow, borehole water level and microseismic energy were chosen as multifactor characteristic variables. Using these variables, this study developed the SSA-CG-Attention multifactor prediction model for mine water inflow along mining faces. The new model extracted effective nonlinear local features of data utilizing a new network structure, which was formed by integrating a convolutional neural network (CNN) based on the time sequence features extracted with a gated recurrent unit (GRU). Furthermore, this model introduced the attention mechanism to focus on input elements during prediction, thus improving the prediction accuracy. Finally, the sparrow search algorithm (SSA) was employed to optimize the model parameters and avoid the occurrence of locally optimal solutions. The new model was compared with traditional single-factor prediction models, including BP neural network, LSTM, and GRU, and multifactor prediction models, consisting of MLP, SLP, SVR, LSTM, GRU, SSA-LSTM, and SSA-GRU. The results indicate that the SSA algorithm allowed for quick optimization within the fewest iterations, thus ruling out the possibility of locally optimal solutions. The new model yielded an absolute error (E_MA), a root mean square error (E_RMS), and a mean absolute percentage error (E_MAP) of 5.24 m³/h, 7.25 m³/h, and 6%, respectively, with a variance sum of 8.9. Furthermore, this model exhibited higher prediction accuracy than other prediction models, and the multifactor prediction models yielded more stable predicted results compared to the single-factor ones. The results of this study provide a new philosophy and methodology for the prediction of mine water inflow along mining faces and serve as a reference and guide for its prediction, prevention, and control, holding theoretical and practical significance.

Keywords

prediction of mine water inflow, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Attention mechanism, multifactor prediction, microseismic energy

DOI

10.12363/issn.1001-1986.23.07.0422

Recommended Citation

DING Yingying, YIN Shangxian, LIAN Huiqing, et al. (2024) "Prediction of mine water inflow along mining faces using the SSA-CG-Attention multifactor model," Coal Geology & Exploration: Vol. 52: Iss. 4, Article 12.
DOI: 10.12363/issn.1001-1986.23.07.0422
Available at: https://cge.researchcommons.org/journal/vol52/iss4/12

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