Integrating a Hybrid Model of Machine Learning and Fuzzy Inference Systems for Enhanced Occupational Risk Assessment in Underground Mining

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dc.contributor.authorCinar, Ulas
dc.contributor.authorBarisik, Tolga
dc.date.accessioned2026-02-03T12:03:09Z
dc.date.available2026-02-03T12:03:09Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description2025 International Conference on Intelligent and Fuzzy Systems-INFUS-Annual -- JUL 29-31, 2025 -- Istanbul, TURKIYE
dc.description.abstractThis study addresses the critical need for advanced risk assessment in unde ground mine sites by proposing a hybrid model of two machine learning algorithms: Random Forest (RF) and Support Vector Regression (SVM) and Fuzzy Inference System (FIS). Building on the success of these models, the given study aims to standardize the assessment of occupational health and safety risks in activities conducted in underground mines using an artificial expert system. Initially, datasets were created by leveraging real expert judgments for specific conditions. Subsequently, these datasets were employed in training the SVM and RF machine learning algorithms to create a decision support system. This decision support system linguistically expresses the probability of risk occurence by utilizing the severity of damage and combinations of potential hazards and processes based on the conditions of the conducted activity. Linguistic expressions were matched with triangular fuzzy numbers and the numerical repr sentation of risk situations in activities was determined employing the princples of fuzzy inference systems. Based on the data obtained, activities were priortized according to the risk levels they encompass. Risk mitigation planning was then organized based on prioritization, addressing more risky activities before those with lower risk levels. The results of this study offer a practical framework for assessing and addressing risks in real-world scenarios, providing valuable insights for effective risk management in underground mining operations.
dc.identifier.doi10.1007/978-3-031-97985-9_76
dc.identifier.endpage697
dc.identifier.isbn978-3-031-97984-2
dc.identifier.isbn978-3-031-97985-9
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-105012924660
dc.identifier.scopusqualityQ4
dc.identifier.startpage688
dc.identifier.urihttps://doi.org/10.1007/978-3-031-97985-9_76
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34986
dc.identifier.volume1528
dc.identifier.wosWOS:001587459200076
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofIntelligent and Fuzzy Systems, Infus 2025, Vol 1
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260130
dc.subjectDecision Making
dc.subjectFuzzy Inference System
dc.subjectMachine Learning
dc.subjectOccupational Health and Safety
dc.subjectRisk Assessment
dc.subjectUnderground Mining
dc.titleIntegrating a Hybrid Model of Machine Learning and Fuzzy Inference Systems for Enhanced Occupational Risk Assessment in Underground Mining
dc.typeConference Object

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