S Bahrami - School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran- Mine Environment and Hydrogeology Research Laboratory, University of Tehran, Tehran, Iran
F Doulati Ardejani - School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran- Mine Environment and Hydrogeology Research Laboratory, University of Tehran, Tehran, Iran

In this study, a hybrid intelligent model was designed to predict groundwater inflow to a mine pit during its advance. The 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 of predicting the groundwater inflow to mine pit. The accuracy and reliability of the model was verified by field data. The predicted results were very close to the field data. The correlation coefficient (R) value was 0.998 for the training set, and in testing stage it was 0.99.

Groundwater Inflow, Mine Pit, Genetic Algorithm, Artificial Neural Network, Hybrid Model