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

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  • Mostafaei, Kamran
  • maleki, Shaho
  • Zamani Ahmad Mahmoudi, Mohammad
  • Knez, Dariusz

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.

Suggested Citation

  • Mostafaei, Kamran & maleki, Shaho & Zamani Ahmad Mahmoudi, Mohammad & Knez, Dariusz, 2022. "Risk management prediction of mining and industrial projects by support vector machine," Resources Policy, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722002677
    DOI: 10.1016/j.resourpol.2022.102819
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    References listed on IDEAS

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    Cited by:

    1. Ronyastra, I Made & Saw, Lip Huat & Low, Foon Siang, 2024. "Monte Carlo simulation-based financial risk identification for industrial estate as post-mining land usage in Indonesia," Resources Policy, Elsevier, vol. 89(C).
    2. Mohammad Ahmad Mahmoudi Zamani & Dariusz Knez, 2023. "Experimental Investigation on the Relationship between Biot’s Coefficient and Hydrostatic Stress for Enhanced Oil Recovery Projects," Energies, MDPI, vol. 16(13), pages 1-13, June.
    3. Mitra Khalilidermani & Dariusz Knez, 2023. "A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering," Energies, MDPI, vol. 16(11), pages 1-23, May.
    4. Senses, Sena & Kumral, Mustafa, 2023. "Embedding extreme events to mine project planning: Implications on cost, time, and disclosure standards," Resources Policy, Elsevier, vol. 86(PA).
    5. Dariusz Knez & Omid Ahmad Mahmoudi Zamani, 2023. "Up-to-Date Status of Geoscience in the Field of Natural Hydrogen with Consideration of Petroleum Issues," Energies, MDPI, vol. 16(18), pages 1-17, September.

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