Neuro-fuzzy prediction model of occupational injuries in mining

Ivaz, Jelena; Petrović, Dejan; Stojadinović, Saša; Stojković, Pavle; Petrović, Sanja; Zlatanović, Dragan

doi:10.1080/10803548.2024.2401678

Article Metadata only M23

2024

Ivaz, Jelena
Petrović, Dejan
Stojadinović, Saša
Stojković, Pavle
Petrović, Sanja
Zlatanović, Dragan

Taylor & Francis

International Journal of Occupational Safety and Ergonomics

English

M23 - Paper in an international journal

Objectives. 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.

coal mine, occupational injury prevention, neural networks, fuzzy logic theory

DOI

10.1080/10803548.2024.2401678

ISSN

1080-3548
2376-9130

URI

https://repozitorijum.tfbor.bg.ac.rs/handle/...

Scopus

OpenCitations

dc.rights.license	ARR
dc.date.available	2025-05-09T10:46:00Z
dc.date.issued	2024
dc.identifier.issn	1080-3548
dc.identifier.issn	2376-9130
dc.identifier.doi	10.1080/10803548.2024.2401678
dc.identifier.uri	https://repozitorijum.tfbor.bg.ac.rs/handle/123456789/5967
dc.description.abstract	Objectives. 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.language.iso	en
dc.publisher	Taylor & Francis
dc.source	International Journal of Occupational Safety and Ergonomics
dc.rights.uri	All rights reserved
dc.subject	coal mine
dc.subject	occupational injury prevention
dc.subject	neural networks
dc.subject	fuzzy logic theory
dc.title	Neuro-fuzzy prediction model of occupational injuries in mining
dc.type	article
dc.type.version	publishedVersion
dc.citation.volume	31
dc.citation.issue	1
dc.citation.spage	24
dc.creator	Ivaz, Jelena
dc.citation.epage	33
dc.citation.rank	M23
dc.creator	Petrović, Dejan
dc.creator	Stojadinović, Saša
dc.creator	Stojković, Pavle
dc.creator	Petrović, Sanja
dc.creator	Zlatanović, Dragan
dc.date.accessioned	2025-05-09T10:46:00Z


Neuro-fuzzy prediction model of occupational injuries in mining	2