Application of Artificial Intelligence in Predicting Coal Mine Disaster Risks: A Review.

Area of Science:

• Mining Engineering
• Computer Science
• Artificial Intelligence

Background:

• Coal mine safety management faces challenges due to complex, interrelated disaster risks.
• Current prediction systems are limited by fragmented data, poor mechanistic understanding, and inadequate early warnings.

Purpose of the Study:

• To explore the application of artificial intelligence (AI) in enhancing coal mine safety prediction.
• To review AI-based approaches for forecasting major coal mining hazards.

Main Methods:

• Review of AI techniques including machine learning, deep learning, and Large Language Models.
• Analysis of AI applications for forecasting coal and gas outbursts, mine fires, water disasters, roof collapses, and dust disasters.
• Examination of technical principles, application scenarios, and empirical outcomes of AI in mine safety.

Main Results:

• AI significantly improves the accuracy of risk prediction in coal mines.
• AI facilitates better data integration and enables more informed safety decision-making.
• AI approaches show promise in addressing complex, interrelated disaster risks.

Conclusions:

• AI, particularly large models and autonomous agents, is crucial for developing advanced coal mine safety and early warning systems.
• Further development and implementation of AI are needed to overcome current challenges in mine safety prediction.
• AI offers a pathway to more effective and proactive safety management in the complex coal mining environment.