Abstract:

In order to prevent the safety accident caused by tailings dam breaks, correlation analysis was conducted on the depth of online monitoring data. A safety situation prediction method of the saturation line of the tailings dam was proposed, which integrated CNN and GRU, so as to grasp the stability and safety development trend of the dam. By comprehensively considering the complex nonlinear and time-series correlation characteristics of monitoring data of the tailings dam, a one-dimensional CNN network was introduced to obtain the local correlation characteristics and spatial characteristics among the multi-source data. Then the GRU model was used to extract the time-series characteristics of the saturation line data. AdamW was used to optimize the adaptability of the model gradient, so as to improve the generalization ability and prediction accuracy of the prediction model. The method was tested and verified in the tailings dam of a metal open-pit molybdenum mine in Luoyang, Henan Province. The results show that compared with the traditional back propagation (BP) neural network, long short-term memory (LSTM) network, GRU, and other prediction models, the proposed prediction model reaches 0.013 915 62, 0.005 432, 0.000 045, 0.006 702, and 0.998 334 for the key indices of mean absolute percentage error (MAPE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R², respectively. The method thus can rapidly and accurately predict saturation line changes.

Key words: convolutional neural network (CNN), gated recurrent unit (GRU), tailings dam, saturation line, Adam weight decay optimizer (AdamW)