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

Accurate prediction of mine water inflow is of great significance for mine water control and efficient and safe production. In order to solve the problem of mine water surge prediction, this paper introduces deep learning theory, combines long short-term memory network （LSTM） and gated circulation unit （GRU）, selects mine water surge as the research object, and establishes a mine water surge prediction model based on LSTM-GRU. Taking the mine water inflow of a mine in Shaanxi Province as sample data, 7 is adopted: The data set was divided into a training set and a test set, 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.