Risk management prediction of mining and industrial projects by support vector machine

Kamran Mostafaei, Shaho Maleki, Mohammad Zamani Ahmad Mahmoudi and Dariusz Knez

Resources Policy, 2022, vol. 78, issue C

Abstract: This research was conducted to predict the financial perspective of Helichal granite mine using Support Vector Machine (SVM) for an exploitation duration of thirty years. The Helichal granite mine is located in Mazandaran province, Iran, and it is currently being exploited through the open-pit mining technique. For the conduction of this research, initially, the financial data related to the exploitation operations in the previous ten years was collated. Then, two variables including the annual production and sale price were determined as the uncertain parameters. Afterward, one hundred simulations of net present value (NPV) were created using Monte Carlo technique. From those simulations, seventy records were adopted to train the SVM model, and the rest (thirty records) were used as the test data. Therefore, thirty NPVs were predicted through the created SVM model. All of the predicted NVPs confirmed that the mining activity is profitable for the relevant thirty years. Furthermore, those NPVs were compared with the corresponding Monte Carlo simulations to validate the accuracy of the results obtained from the SVM model. The results indicated a close correlation of determination equal to 96% between the SVM-predicted NPVs, and the Monte Carlo-simulated NPVs. Hence, it was concluded that the SVM approach is highly reliable to anticipate the financial profitability of mining projects as well as other identical industrial plans.

Keywords: Risk management; Mining economy; Financial analysis; Support vector machine; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722002677

DOI: 10.1016/j.resourpol.2022.102819

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