Prediction of the Groundwater Rebound Process in a Backfilled Open Cut Mine Using an Artificial Neural Network

Ardejani, Faramarz Doulati; Sadeghiamirshahidi, Mohammadhossein; Singh, R. N.; Kish, Teimour Eslam; Reed, S. M.

doi:10.1007/s10230-013-0243-7

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…Adamowski and Sun, 2010;Banerjee et al, 2011;Belayneh et al, 2014;Cannas et al, 2005;Chen and Chang, 2009;Chiang et al, 2004;Doulati Ardejani et al, 2013;Imrie et al, 2000;Ketabchi and Ataie-Ashtiani, 2015;Khalil et al, 2011;Sivakumar et al, 2002;Yoon et al, 2011).…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…An ANN is an empirical modelling tool that is based on the behaviour of biological neural structures (Doulati Ardejani et al, 2013;Yao et al, 2005) and the use of such networks is rapidly increasing, especially in process modelling, simulation and predictions (Banerjee et al, 2011;Doulati Ardejani et al, 2013;Khalil et al, 2011;Sadeghiamirshahidi and Ardejani, 2013;Yoon et al, 2011).…”

Section: Undesirable Effects Of This May Includementioning

confidence: 99%

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Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine

Bahrami

Ardejani

Baafi

2016

Journal of Hydrology

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(2016). Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine. Journal of Hydrology, Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine AbstractIn this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models. AbstractIn this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known ana...

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