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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

Journal Article


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Abstract


  • In 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.

UOW Authors


  •   Bahrami, Saeed (external author)
  •   Doulati Ardejani, Faramarz (external author)
  •   Baafi, Ernest

Publication Date


  • 2016

Citation


  • Bahrami, S., Doulati Ardejani, F. & Baafi, E. (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, 536 471-484.

Scopus Eid


  • 2-s2.0-84962581935

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6304&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5276

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 471

End Page


  • 484

Volume


  • 536

Place Of Publication


  • Netherlands

Abstract


  • In 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.

UOW Authors


  •   Bahrami, Saeed (external author)
  •   Doulati Ardejani, Faramarz (external author)
  •   Baafi, Ernest

Publication Date


  • 2016

Citation


  • Bahrami, S., Doulati Ardejani, F. & Baafi, E. (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, 536 471-484.

Scopus Eid


  • 2-s2.0-84962581935

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6304&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5276

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 471

End Page


  • 484

Volume


  • 536

Place Of Publication


  • Netherlands