Financial Risk Management Prediction of Mining and Industrial Projects using Combination of Artificial Intelligence and Simulation Methods

Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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JR_JMAE-13-4_018

تاریخ نمایه سازی: 12 بهمن 1401

Abstract:

Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since several uncertain parameters are incorporated in the modelling process, distribution functions are employed to explain the parameters. However, due to the usual constrain of limited data, these functions cannot significantly explain the variation of those uncertain parameters. Support vector machine, one of the efficient techniques of artificial intelligence, provides the appropriate results in the classification and regression tasks. The principal aims of this research work are to integrate the simulation and artificial intelligence methods to manage the risk prediction of an economic system under uncertain conditions. The financial process of the Halichal mine in the Mazandaran province, Iran, is considered a case study to prove the performance of the support vector machine technique. The results show that integrating the simulation and support vector machine techniques can provide more realistic results, especially when including uncertain parameters. The correlation between the net present value obtained from the simulation and the net present value is about ۰.۹۶, which shows the capability of artificial intelligence methods and the simulation process. The root mean square error of the support vector machine prediction is about ۰.۳۲۲, which indicates a low error rate in the net present value estimation. The values of these errors prove that this method has a high accuracy and performance for predicting a net present value in the Halichal granite mine.

Authors

Sirvan Moradi

Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

Seyed Davoud Mohammadi

Department of Geology, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran

Abbas Aghajani Bazzazi

Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

Ali Aali Anvari

Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

Ava Osmanpour

Department of Geology, Faculty of Science, Bu-Ali Sina University, Hamedan, Iran

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