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The prediction of water inflow during coal mining is an important issue. There are many factors that can affect the water inflow in mines. The intercoupling of these factors makes it difficult for current water inflow forecasting methods to meet the needs of real-time forecasting. Open channels are the main devices used for mine drainage, and their flow rate reflects the water inrush of a mine to some extent. This paper uses a hybrid neural network model combining attention mechanisms and a gated recurrent unit network to make real-time predictions of open channel flow. First, attention mechanisms are used to learn the interdependence between multisource hydrosensor data, and then, a gated recurrent unit network is employed to capture the dependencies on different time scales to improve the prediction accuracy of the neural network model. Finally, we design a series of comparative experiments to verify and analyse the performance of the hybrid neural network model. The experimental verification shows that the proposed model can learn the dependency relationships among multisource sensors, and the modelling of these dependencies can greatly improve the prediction accuracy of real-time flow in open channels.

A Gated Recurrent Unit Network Model for Predicting Open Channel Flow in Coal Mines Based on Attention Mechanisms