在矿井实际生产过程开始前，准确预测涌水量对预防矿井潜在水害事故和保障安全生产具有重要的直接指导作用。

为提升以大气降水为主要补给来源的露天矿井涌水量预测的准确性与稳定性，提出一种结合双向循环神经网络(bidirectional recurrent neural network，Bi-RNN)和地下水数值模拟系统(groundwater modeling system，GMS)的涌水量预测耦合模型。该模型通过对全球预报系统数据(global forecast system，GFS)提供的研究区内历史预报降水与实际降水之间差值的波动规律进行分析，利用Bi-RNN对预报降水数据进行校正，将校正后的降水数据输入GMS中以预测南北2个开采区的涌水量。同时，采用传统的大井法和补给模数大井法对开采区涌水量进行预测，并对比不同方法的预测结果。

结果表明：北部开采区耦合模型预测结果为294 m³/d，大井法预测结果为276.651~940.613 m³/d，补给模数大井法预测结果为287.241 m³/d；南部开采区耦合模型预测结果为1160 m³/d；大井法预测结果为3330.107~5090.944 m³/d，补给模数大井法预测结果为1108.575 m³/d。研究表明，所建立的耦合模型在预测露天矿井涌水量方面取得了一定成果，作为一种结合多数据源的预测方法具有一定优势。该模型为解决矿井涌水量问题提供了新的思路和技术支持，具有较高的理论价值和实际应用潜力。

Accurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production.

To enhance the prediction accuracy and stability of water yield of open-pit metal mines, for which atmospheric precipitation acts as the primary recharge source of water, this study developed a prediction model that coupled a bidirectional recurrent neural network (Bi-RNN) and the Groundwater Modeling System (GMS) software. Specifically, based on historical forecasted precipitation data provided by the Global Forecast System (GFS), the fluctuation pattern of differences between predicted forecasted and actual precipitation was analyzed. After being corrected using the Bi-RNN, the forecasted precipitation data were input into GMS for prediction. The coupling model was employed to predict water yield of mine in the northern and southern mining areas in the study area. Concurrently, the water yield of mine in the mining areas was also predicted using both the traditional large diameter well method and the recharge modulus large diameter well method. Finally, the prediction results based on the three methods were compared.

The results indicate that the coupling model, the traditional large diameter well method, and the recharge modulus large diameter well method yielded water yield of mine of 294 m³/d, 276.651 to 940.613 m³/d, and 287.241 m³/d, respectively for the northern mining area and 1160 m³/d, 3330.107 to 5090.944 m³/d, and 1108.575 m³/d, respectively for the northern mining areas. These results suggest that the proposed coupling model, a prediction method combining multiple data sources, has achieved certain results and enjoys certain advantages in predicting water yield of mine. This model provides a new philosophy and technical support for predicting water yield of mine, exhibiting high theoretical value and great potential for practical application.