Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model
[Objective]Accurately predicting water yield of mine before mining can provide directive guidance for pre-venting potential water hazards and ensuring safe production.[Methods]To enhance the prediction accuracy and stabil-ity of water yield of open-pit metal mines,for which atmospheric precipitation acts as the primary recharge source of wa-ter,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 pre-cipitation 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 tradi-tional large diameter well method and the recharge modulus large diameter well method.Finally,the prediction results based on the three methods were compared.[Results and Conclusions]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 1 160 m3/d,3 330.107 to 5 090.944 m3/d,and 1 108.575 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 en-joys 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.
open-pit minepredicted water yield of minebidirectional recurrent neural network(Bi-RNN)global fore-cast system(GFS)groundwater modeling system(GMS)deep learning
万方数据
维普
