Multi-Factor Prediction of Water Inflow from the Working Face Based on an Improved SSA-RG-MHA Model

Abstract

The accurate prediction of mine water inflow is very important for mine design and safe production. The existing forecasting methods based on single factors are often less accurate and stable. Multi-factor data-driven models play a key role in predicting water inflow without taking physical changes into account. Therefore, a multi-factor prediction method based on an improved SSA-RG-MHA model is introduced in this study. The model uses two sets of data related to water inflow as the input to improve prediction accuracy and stability. The model first applies a residual network (ResNet) to mitigate the problems of disappearing gradients and explosions. Gated Recurrent Units (GRUs) are then used to learn the characteristics of long-term sequence data. The model combines ResNet and GRU into a new network architecture and incorporates a multiple attention (MHA) mechanism to focus on information at different time scales. Finally, the optimized sparrow search algorithm (SSA) is used to optimize the network parameters to improve the global search ability and avoid local optimization. The mine water inflow is affected by many factors, among which the water level and microseismic energy data are particularly important. Therefore, these data types are selected as the key variables of mine water inflow prediction. The experimental results show that the improved SSA-RG-MHA model significantly reduces the prediction error: the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were reduced to 4.42 m3/h, 7.17 m3/h, and 5%, respectively. The multi-factor water inflow prediction method is more stable and reliable than single-factor models as it comprehensively considers the factors affecting the water inflow of the working face. Compared with other multi-factor models, this model exhibits higher prediction accuracy and robustness, providing a basis for mine water hazard monitoring and early warning.