Aquifer water yield property prediction based on a hybrid neural network model: a case of yili no.4 colliery, Xinjiang - Yang, Jielin; Li, Wenping; Zhu, Jingzhong; Li, Dongding (Frontiers in Earth Science, 2025)
With the gradual increase of coal production capacity, the mining-induced roof water damage has become increasingly prominent. Accurately and effectively predicting the water yield property of the roof aquifer based on the hydrogeological data is of great significance for preventing and controlling mine water damage. In this study, we select six evaluation factors, including aquifer thickness (AT), permeability coefficient (PC), coring rate (CR), rock brittleness-plasticity ratio (RBPR), equivalent thickness of sandstone (ETS), and fold undulation (FU). A hybrid model is proposed, integrating the convolutional neural networks (CNN) with long short-term memory (LSTM) optimized by the Attention module to improve the model’s performance. The model is applied to predict the water yield property of the Paleocene aquifer in the Yili No. 4 colliery by collecting 100 hydrogeological datasets. The model is trained to predict the unit water inflow (UWI) of the roof aquifer, reflecting the water yield property. Besides, comparative analysis with the CNN, LSTM, and the CNN-LSTM models demonstrates that the prediction performance of the CNN-LSTM-Attention model outperforms the three contrastive models. The CNN-LSTM-Attention hybrid model achieves higher prediction accuracy. This study proposes a scientifically robust evaluation method for delineating WYPZ in mining areas with limited hydrogeological exploration data.