Research on mine water inflow prediction method of LSTM-GRU composite model based on deep learning

Abstract

In order to solve the problem of mine water surge prediction, we introduce deep learning theory, combine long short-term memory network (LSTM) and gated circulation unit (GRU), select mine water surge as the research object, and establish a mine water surge prediction model based on LSTM-GRU. Taking the mine water inflow of a mine in Shaanxi Province as sample data, the data set was divided into a training set and a test set using a 7∶3 ratio, and the gradient descent algorithm with good model training effect was selected to determine the network model parameters and regularization parameters. In order to prove the prediction accuracy of the LSTM-GRU model, the prediction results were compared with those obtained by the traditional ARIMA model and the LSTM model to predict mine water gusher, respectively. The results show that: the mean absolute percentage error (RMSE), root mean square error (MAE), mean absolute error (MAPE) and coefficient of determination (R2) of the LSTM-GRU composite model are 70.51, 53.4, 2.80% and 0.86, indicating that the model has high prediction accuracy and reliability. The prediction effect is better than the traditional ARIMA model and LSTM model.

Keywords

• mine water control
• mine water flow prediction
• lstm-gru network model
• arima model
• lstm model