Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model
ObjectiveAccurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production. MethodsTo enhance the prediction accuracy and stability of water yield of open-pit metal mines, for which atmospheric precipitation act...
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Editorial Office of Coal Geology & Exploration
2024-12-01
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| Series: | Meitian dizhi yu kantan |
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| Online Access: | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.07.0489 |
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| author | Yuxing ZHAO Xiangwen LI |
| author_facet | Yuxing ZHAO Xiangwen LI |
| author_sort | Yuxing ZHAO |
| collection | DOAJ |
| description | ObjectiveAccurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production. MethodsTo 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. Results and ConclusionsThe 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. |
| format | Article |
| id | doaj-art-fb6990e7f5ef44ac94c2c6c0ab0ec417 |
| institution | DOAJ |
| issn | 1001-1986 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Coal Geology & Exploration |
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| series | Meitian dizhi yu kantan |
| spelling | doaj-art-fb6990e7f5ef44ac94c2c6c0ab0ec4172025-08-20T02:39:59ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862024-12-01521215516910.12363/issn.1001-1986.24.07.048924-07-0489zhaoyuxingPredicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling modelYuxing ZHAO0Xiangwen LI1School of Environmental Studies, China University of Geosciences, Wuhan 430074, ChinaCollege of Mining Engineering, Heilongjiang University of Science and Technology, Harbin 150028, ChinaObjectiveAccurately predicting water yield of mine before mining can provide directive guidance for preventing potential water hazards and ensuring safe production. MethodsTo 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. Results and ConclusionsThe 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.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.07.0489open-pit minepredicted water yield of minebidirectional recurrent neural network (bi-rnn)global forecast system (gfs)groundwater modeling system (gms)deep learning |
| spellingShingle | Yuxing ZHAO Xiangwen LI Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model Meitian dizhi yu kantan open-pit mine predicted water yield of mine bidirectional recurrent neural network (bi-rnn) global forecast system (gfs) groundwater modeling system (gms) deep learning |
| title | Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model |
| title_full | Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model |
| title_fullStr | Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model |
| title_full_unstemmed | Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model |
| title_short | Predicted water yield of open-pit metal mines based on a Bi-RNN and GMS coupling model |
| title_sort | predicted water yield of open pit metal mines based on a bi rnn and gms coupling model |
| topic | open-pit mine predicted water yield of mine bidirectional recurrent neural network (bi-rnn) global forecast system (gfs) groundwater modeling system (gms) deep learning |
| url | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.07.0489 |
| work_keys_str_mv | AT yuxingzhao predictedwateryieldofopenpitmetalminesbasedonabirnnandgmscouplingmodel AT xiangwenli predictedwateryieldofopenpitmetalminesbasedonabirnnandgmscouplingmodel |