TY - JOUR ID - 330 TI - Application of artificial neural network and genetic algorithm to modelling the groundwater inflow to an advancing open pit mine JO - Journal of Mining and Environment JA - JME LA - en SN - 2251-8592 AU - Bahrami, S. AU - Doulati Ardejani, F. AD - School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran AD - Mine Environment and Hydrogeology Research Laboratory, University of Tehran, Tehran, Iran Y1 - 2014 PY - 2014 VL - 6 IS - 1 SP - 21 EP - 30 KW - Groundwater inflow KW - Mine Pit KW - Genetic Algorithm KW - Artificial Neural Network KW - Hybrid Model DO - 10.22044/jme.2014.330 N2 - 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. UR - https://jme.shahroodut.ac.ir/article_330.html L1 - https://jme.shahroodut.ac.ir/article_330_a080189c28efb629c5ee4cd88aec714d.pdf ER -