Neuro-fuzzy prediction model of occupational injuries in mining

dc.citation.epage33
dc.citation.issue1
dc.citation.rankM23
dc.citation.spage24
dc.citation.volume31
dc.contributor.authorIvaz, Jelena
dc.contributor.authorPetrović, Dejan
dc.contributor.authorStojadinović, Saša
dc.contributor.authorStojković, Pavle
dc.contributor.authorPetrović, Sanja
dc.contributor.authorZlatanović, Dragan
dc.date.accessioned2025-05-09T10:46:00Z
dc.date.available2025-05-09T10:46:00Z
dc.date.issued2024
dc.description.abstractObjectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being.
dc.identifier.doi10.1080/10803548.2024.2401678
dc.identifier.issn1080-3548
dc.identifier.issn2376-9130
dc.identifier.urihttps://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5967
dc.language.isoen
dc.publisherTaylor & Francis
dc.rights.licenseARR
dc.rights.uriAll rights reserved
dc.sourceInternational Journal of Occupational Safety and Ergonomics
dc.subjectcoal mine
dc.subjectoccupational injury prevention
dc.subjectneural networks
dc.subjectfuzzy logic theory
dc.titleNeuro-fuzzy prediction model of occupational injuries in mining
dc.typearticle
dc.type.versionpublishedVersion

Files

Collections