|تعداد مشاهده مقاله||103,282,307|
|تعداد دریافت فایل اصل مقاله||81,345,691|
Comparison between the performance of four metaheuristic algorithms in training a multilayer perceptron machine for gold grade estimation
|International Journal of Mining and Geo-Engineering|
|مقاله 2، دوره 56، شماره 2، شهریور 2022، صفحه 97-105 اصل مقاله (1.28 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/ijmge.2021.314154.594880|
|Andisheh Alimoradi 1؛ Hossein Hajkarimian1؛ Hamidreza Hemati Ahooi1؛ Mohammad Salsabili2|
|1Department of Mining and Petroleum Engineering, Faculty of Engineering, Imam Khomeini International University|
|2Departement des sciences appliquees, Universite du Quebec a Chicoutimi, Quebec, Canada|
|Reserve evaluation is a very difficult and complex process. The most important and yet most challenging part of this process is grade estimation. Its difficulty derived from challenges in obtaining required data from the deposit by drilling boreholes, which is a very time consuming and costly act itself. Classic methods which are used to model the deposit are based on some preliminary assumptions about reserve continuity and grade spatial distribution which are not true about all kind of reserves. In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) is applied to solve the problem of ore grade estimation of highly sparse data from zarshouran gold deposit in Iran. The network is trained using four metaheuristic algorithms in separate stages for each algorithm. These algorithms are artificial bee colony (ABC), genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). The accuracy of predictions obtained from each algorithm in each stage of experiments were compared with real gold grade values. We used unskillful value to check the accuracy and stability of each network. Results showed that the network trained with ABC algorithm outperforms other networks that trained with other algorithms in all stages having least unskillful value of 13.91 for validation data. Therefore, it can be more suitable for solving the problem of predicting ore grade values using highly sparse data.|
|multilayer perceptron؛ metaheuristic machine learning؛ grade estimation؛ inverse modeling؛ optimization|
 Abraham, A., H. Guo, and H. Liu, Swarm Intelligence: Foundations, Perspectives, and Applications, in Swarm Intelligent Systems, N. Nedjah and L.d.M. Mourelle, Editors. 2006, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 3-25.
 Alimoradi, A., Maleki, B., Karimi, A., Sahafzadeh, M., Abbasi, S. Integrating Geophysical Attributes with New Cuckoo Search Machine-Learning Algorithm to Estimate Silver Grade Values–Case Study: Zarshouran Gold Mine, Journal of Mining and Environment, 2020, Vol. 11, No. 3, 865-879.
 Atashpaz-Gargari, E. and C. Lucas, Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. Vol. 7. 2007. 4661-4667.
 Badel, M., S. Angorani, and M. Shariat Panahi, The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit. Computers & Geosciences, 2011. 37(4): p. 530-540.
 Bárdossy, G. and J. Fodor, Evaluation of Uncertainties and Risks in Geology. 2004, Springer Berlin Heidelberg.
 Chatterjee, S., Ore grade estimation of a limestone deposit in India using an Artificial Neural Network. 2006.
 Daliran, F., Agdarreh & Zarshuran SRHDG deposits, Takab region, NW-Iran. in Proceedings: GSA - Annual Meeting, Fall 2002.
 Daliran, F., Discovery of 1.2 kg/t gold and 1.9 kg/t silver in mud precipitates of a cold spring from the Takab geothermal field, NW Iran, in Mineral Exploration and sustainable development, Vol.1. Ed.: D.G. Eliopoulos. 2003, Millpress, Rotterdam. p. 461-464.
 Dutta, S., Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data. Vol. 2. 2010. 86-96.
 Eberhart, R. and J. Kennedy, A new optimizer using particle swarm theory, in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (2015). IEEE.
 Goldberg, D.E. and J.H. Holland, Genetic Algorithms and Machine Learning. Machine Learning, 1988. 3(2): p. 95-99.
 Gopalakrishnan, K., Particle Swarm Optimization in Civil Infrastructure Systems: State-of-the-Art Review, in Metaheuristic Applications in Structures and Infrastructures, A.H. Gandomi, et al., Editors. 2013, Elsevier: Oxford. p. 49-76.
 Haykin, S.S., Neural networks: a comprehensive foundation. 1999, Upper Saddle River, N.J.: Prentice-Hall.
 Jalloh, A.B., Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study. International Journal of Mining Science and Technology, 2016. 26(4): p. 581-585.
 Journel, A.G. and C.J. Huijbregts, Mining geostatistics. 1978, New York: The Blackburn Press.
 Kapageridis, I., Application of artificial neural network systems to ore grade estimation from exploration data. 1999, University of Nottingham. p. 14-35.
 Kapageridis, I.K. and B.H. Denby. Ore Grade Estimation with Modular Neural Network Systems – A Case Study. 1999.
 Karaboga, D., An idea based on honey bee swarm for numerical optimization, Technical Report TR06, Erciyes University, Kayseri, Turkey. 2005.
 Karaboga, D., B. Akay, and C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, in Modeling Decisions for Artificial Intelligence. 2007, Springer Berlin Heidelberg. p. 318-329.
 Koike, K., Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District, Northern Japan, for Exploring Kuroko-Type Deposits. Vol. 11. 2002, Natural Resources Research. 135-156.
 Li, X.-l., Adaptive ore grade estimation method for the mineral deposit evaluation. Mathematical and Computer Modelling, 2010. 52(11-12): p. 1947-1956.
 Maleki, S., H. Ramazi, and S. Moradi, Estimation of Iron Concentration by Using a Support Vector Machine and an Artificial Neural Network - the Case Study of the Choghart Deposit southeast of Yazd, Yazd, Iran. Geopersia, 2014. 4(2): p. 75-86.
 Edwards, R. Atkinson, K., Ore Deposit Geology and its Influence on Mineral Exploration, 1986, Springer
 Paar, W., Daliranite, PbHgAs2S6, a new sulphosalt from the Zarshouran Au-As deposit, Takab region, Iran. Mineralogical Magazine, 2009. Vol. 73(5): p. 871–881.
 Paravarzar, S., Correlation between geological units and mineralized zones using fractal modeling in Zarshuran gold deposit (NW Iran). Arabian Journal of Geosciences, 2014. 8: p. 3845-3854.
 Pyrcz, M. J., Gringarten, E., Frykman, P., Deutsch, C. V., Representative Input Parameters for Geostatistical Simulation, 2006, University of Alberta
 Strebelle, S., Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics. Mathematical Geology, 2002. 34(1): p. 1-21.
 Tahmasebi, P. and A. Hezarkhani, Application of a Modular Feedforward Neural Network for Grade Estimation. Natural Resources Research. Vol. 20. 2011. 25-32.
 Talbi, E.-G., Metaheuristics: From Design to Implementation. 2009: Wiley Publishing. 593.
 Samanta, B., Sparse Data Division Using Data Segmentation and Kohonen Network for Neural Network and Geostatistical Ore Grade Modeling in Nome Offshore Placer Deposit. Natural Resources Research, 2004. 13(3): p. 189-200.
 Samanta, B., R. Ganguli, and S. Bandopadhyay, Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit. Mining Technology, 2005. 114(3): p. 129-139.
 Samanta, B., S. Bandopadhyay, and R. Ganguli, Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation. Mathematical Geology, 2006. 38(2): p. 175-197.
 Samanta, B. and S. Bandopadhyay, Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit. Computers & Geosciences, 2009. 35(8): p. 1592-1602.
 Yang, X.-S., Optimization and Metaheuristic Algorithms in Engineering, in Metaheuristics in Water, Geotechnical and Transport Engineering, X.-S. Yang, et al., Editors. 2013, Elsevier: Oxford. p. 1-23.
تعداد مشاهده مقاله: 195
تعداد دریافت فایل اصل مقاله: 132