Ai powered smart mining safety and risk prediction system

This paper presented SMSRPS — Smart Mining Safety & Risk Prediction System — a complete, modular, production-ready Python application integrating IoT sensor data streams, machine learning hazard classification,  and  real-time  risk  visualization  for underground mine safety monitoring. The system applies Random Forest classification to predict four-class hazard levels with 96.3% accuracy, and uses LSTM-based anomaly detection to provide an average 11.8-minute predictive lead time before threshold breaches. Experimental results demonstrate that the AI-powered prediction engine significantly outperforms conventional threshold-based and rule-based safety systems, reducing false alarm rates by 31.2% while maintaining 97% Emergency event recall. SMSRPS makes three primary contributions: (1) a complete, documented AI safety monitoring framework on standard hardware; (2) a realistic mine sensor dataset with labeled hazard scenarios; and (3) an educational codebase demonstrating Random Forest classification, LSTM anomaly detection, and real-time IoT dashboard integration.

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