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Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method

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dc.citedby9
dc.contributor.authorAbbaszadeh M.en_US
dc.contributor.authorSoltani-Mohammadi S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid54419507900en_US
dc.contributor.authorid54929872400en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T09:36:55Z
dc.date.available2023-05-29T09:36:55Z
dc.date.issued2022
dc.descriptionCopper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iranen_US
dc.description.abstractThe support vector classifier (SVC) is one of the most powerful machine learning algorithms. This algorithm has been accepted as an effective method in three-dimensional geological modeling. Although the model selection has a great impact on the performance of SVC algorithm, most of mining studies have neglected it and used the grid search method. Therefore, in this study, a new approach is proposed for improving the selection of SVC models. This approach uses particle swarm optimization (PSO) to determine the important parameters of SCV such as penalty and kernel parameters. The proposed approach was applied in the modeling process of the Iju porphyry copper deposit to delineate alteration and mineralization zones. The optimal penalty and kernel parameters were found to be 27.2 and 2?4.75 for alteration zone, and 22.72 and 2?6.23 for mineralization zone, respectively. With 97.4% and 97.01% rates of accuracy for mineralization and alteration zones, the PSO results showed reasonable performance in classification. The proposed approach had better accuracy than grid search method. Therefore, because of its better performance, the geological models were developed using the PSO method to be used as a basis for future resource evaluation. � 2022 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo105140
dc.identifier.doi10.1016/j.cageo.2022.105140
dc.identifier.scopus2-s2.0-85130520033
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85130520033&doi=10.1016%2fj.cageo.2022.105140&partnerID=40&md5=20de9ab6cb5e11df0ac08cddcc5b0d49
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26815
dc.identifier.volume165
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleComputers and Geosciences
dc.titleOptimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search methoden_US
dc.typeArticleen_US
dspace.entity.typePublication
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