Application of neural networks to predict net present value in mining projects
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ORIGINAL PAPER
Application of neural networks to predict net present value in mining projects A. R. Sayadi & S. M. M. Tavassoli & M. Monjezi & M. Rezaei
Received: 10 February 2012 / Accepted: 31 October 2012 # Saudi Society for Geosciences 2012
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 (R2 00.987) 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. Keywords Net present value . Artificial neural network . Zarshuran gold mine project
A. R. Sayadi : M. Monjezi (*) Faculty of Engineering, Tarbiat Modares University, Tehran, Iran e-mail: [email protected] S. M. M. Tavassoli School of Business, Curtin University, Sarawak, Malaysia e-mail: [email protected] M. Rezaei Faculty of Engineering, University of Tehran, Tehran, Iran
Introduction In investment projects, discounted cash flow (DCF) analysis is performed. In this analysis, decision-making economic indicators such as net present value (NPV), internal rate of return, pay back period, etc. are estimated amongst which NPV can be considered as the most popular and important criterion (Remer and Nieto 1995; Taggart 1996; Tsao 2012). Normally, estimation of NPV is made under uncertainty conditions, which are obviously associated with the economic input parameters. Effect of these uncertainties on the estimated economic criteria is very important and should be determined (Dowd 1995). In conventional methods, sensitivity analysis is carried out to identify and assess the factors affecting the project success. During this process, the effect of uncertainties is investigated by considering the most probable range of variation for input parameters, which is defined by the economic experts. The declared input variations should be applied in estimating NPV (Jovanovic 1999). It should also be noted that this process is separately repeated for each parameter; however, it is required to realize and understand the combination effect o
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