Application of artificial neural network and genetic algorithm to modelling the groundwater inflow to an advancing open pit mine | ||
| Journal of Mining and Environment | ||
| مقاله 3، دوره 6، شماره 1، فروردین 2015، صفحه 21-30 اصل مقاله (808.83 K) | ||
| نوع مقاله: Case Study | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2014.330 | ||
| نویسندگان | ||
| S. Bahrami1؛ F. Doulati Ardejani* 2 | ||
| 1School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Mine Environment and Hydrogeology Research Laboratory, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (HH) in the observation wells to the distance of observation wells from the centre of pit were used as inputs to the network. An ANN-GA with 4-5-3-1 arrangement was found capable to predict the groundwater inflow to mine pit. The accuracy and reliability of model was verified by field data. Predicted results were very close to the field data. The correlation coefficient (R) value was 0.998 for training set, and in testing stage it was 0.99. | ||
| کلیدواژهها | ||
| Groundwater inflow؛ Mine Pit؛ Genetic Algorithm؛ Artificial Neural Network؛ Hybrid Model | ||
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آمار تعداد مشاهده مقاله: 3,011 تعداد دریافت فایل اصل مقاله: 3,071 |
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