IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v78y2022ics0301420722002677.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420722002677
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2022.102819?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Rafael Rosillo & Javier Giner & David De la Fuente, 2014. "Stock Market Simulation Using Support Vector Machines," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 488-500, September.
    2. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    3. Chi Xie & Changqing Luo & Xiang Yu, 2011. "Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 671-686, April.
    4. Ramasesh, Ranga V. & Jayakumar, Maliyakal D., 1997. "Inclusion of flexibility benefits in discounted cash flow analyses for investment evaluation: A simulation/optimization model," European Journal of Operational Research, Elsevier, vol. 102(1), pages 124-141, October.
    5. Lefley, Frank, 1997. "Approaches to risk and uncertainty in the appraisal of new technology capital projects," International Journal of Production Economics, Elsevier, vol. 53(1), pages 21-33, November.
    6. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
    7. Samis, Michael & Davis, Graham A. & Laughton, David & Poulin, Richard, 2005. "Valuing uncertain asset cash flows when there are no options: A real options approach," Resources Policy, Elsevier, vol. 30(4), pages 285-298, December.
    8. Elmira Emsia & Cagay Coskuner, 2016. "Economic Growth Prediction Using Optimized Support Vector Machines," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 453-462, October.
    9. Amin Gharipour & Morteza Sameti & Ali Yousefian, 2010. "A Comparative Approximate Economic Behavior Analysis of Support Vector Machines and Neural Networks Models," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 15(2), pages 17-40, spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
    2. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    4. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    5. Costa Lima, Gabriel A. & Suslick, Saul B., 2006. "Estimating the volatility of mining projects considering price and operating cost uncertainties," Resources Policy, Elsevier, vol. 31(2), pages 86-94, June.
    6. Fan, Miao & Lin, Neng-Pai & Sheu, Chwen, 2008. "Choosing a project risk-handling strategy: An analytical model," International Journal of Production Economics, Elsevier, vol. 112(2), pages 700-713, April.
    7. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    8. Ming-Fu Hsu & Ping-Feng Pai, 2013. "Incorporating support vector machines with multiple criteria decision making for financial crisis analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3481-3492, October.
    9. Elena Katok & William Tarantino & Terry P. Harrison, 2003. "Investment in production resource flexibility: An empirical investigation of methods for planning under uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(2), pages 105-129, March.
    10. Yoon-Joo Park, 2018. "Predicting the Helpfulness of Online Customer Reviews across Different Product Types," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    11. Abdulkadir Atalan, 2023. "Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 214-241, January.
    12. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    13. Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
    14. Petrakova Aleksandra & Merkurjeva Galina & Affenzeller Michael, 2015. "Heterogeneous versus Homogeneous Machine Learning Ensembles," Information Technology and Management Science, Sciendo, vol. 18(1), pages 135-140, December.
    15. Dimitrakopoulos, Roussos G. & Abdel Sabour, Sabry A., 2007. "Evaluating mine plans under uncertainty: Can the real options make a difference?," Resources Policy, Elsevier, vol. 32(3), pages 116-125, September.
    16. Wang, Xinlin & Ahn, Sung-Hoon, 2020. "Real-time prediction and anomaly detection of electrical load in a residential community," Applied Energy, Elsevier, vol. 259(C).
    17. Feng Shen & Yunwen Ma & Run Wang & Ningning Pan & Zhiyi Meng, 2019. "Does environmental performance affect financial performance? Evidence from Chinese listed companies in heavily polluting industries," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1941-1958, July.
    18. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    19. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
    20. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722002677. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.