Neuro-fuzzy prediction model of occupational injuries in mining
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Date
2024
Authors
Ivaz, Jelena
Petrović, Dejan
Stojadinović, Saša
Stojković, Pavle
Petrović, Sanja
Zlatanović, Dragan
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
Source
International Journal of Occupational Safety and Ergonomics
Volume
31
Issue
1
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.
Description
Keywords
coal mine, occupational injury prevention, neural networks, fuzzy logic theory
Citation
DOI
10.1080/10803548.2024.2401678
Scopus
ISSN
1080-3548
2376-9130
2376-9130
ISBN
License
ARR