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Application of neural networks to predict net present value in mining projects

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Abstract

Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R 2 = 0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV.

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Authors and Affiliations

  1. Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    A. R. Sayadi & M. Monjezi

  2. School of Business, Curtin University, Sarawak, Malaysia

    S. M. M. Tavassoli

  3. Faculty of Engineering, University of Tehran, Tehran, Iran

    M. Rezaei

Authors
  1. A. R. Sayadi
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  2. S. M. M. Tavassoli
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  3. M. Monjezi
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  4. M. Rezaei
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Correspondence to M. Monjezi.

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Sayadi, A.R., Tavassoli, S.M.M., Monjezi, M. et al. Application of neural networks to predict net present value in mining projects. Arab J Geosci 7, 1067–1072 (2014). https://doi.org/10.1007/s12517-012-0750-z

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  • DOI: https://doi.org/10.1007/s12517-012-0750-z

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