Examining the role of AI and machine learning in improving hazard detection and predictive analytics for accident prevention in mining operations

Arthur, Alan Ato and Annankra, Joshua Asiektewen and Yakin, Zakaria (2025) Examining the role of AI and machine learning in improving hazard detection and predictive analytics for accident prevention in mining operations. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 640-646. ISSN 2582-8266

The paper critically reviews the application of Artificial Intelligence and Machine Learning in the mining sector to improve health and safety. Over the years, conventional safety measures have often involved reactive measures. Such traditional hazard detection methods are often disconnected, thus providing only limited safety improvements in the workplace. This paper looks at proactively monitoring health and safety by integrating machine learning and artificial intelligence into conventional systems to significantly improve decision-making, enhance safety, and drive continuous improvement. The review analyzes specific applications, including real-time hazard detection, predictive maintenance, worker behavior analysis, and environmental monitoring. Our findings demonstrate that AI/ML integration enables data-driven decision-making, automated risk assessment, and systematic safety improvements through continuous learning algorithms. This research contributes to the growing body of knowledge on technological innovation in mining safety and provides practical insights for industry stakeholders seeking to modernize their safety management systems.