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Abbas Aghajani Bazzazi

Abbas Aghajani Bazzazi

Assistant Professor

College: Faculty of Engineering

Department: Mining Engineering

Degree: Ph.D

Birth Year: 1980

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Abbas Aghajani Bazzazi

Assistant Professor Abbas Aghajani Bazzazi

College: Faculty of Engineering - Department: Mining Engineering Degree: Ph.D | Birth Year: 1980 |

Financial risk management prediction of mining and industrial projects using combination of artificial intelligence and simulation methods

AuthorsSirvan Moradi- Seyed Davoud Mohammadi- Abbas Aghajani Bazzazi- Ali Aali Anvari- Ava Osmanpour
JournalJournal of Mining and Environment
Presented byKashan
Page number1211-1223
Volume number13
Paper TypeFull Paper
Published At2022-09-01
Journal GradeISI
Journal TypeTypographic
Journal CountryIran, Islamic Republic Of

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 0.96, 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 0.322, 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.

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