Predicting Miner Localization in Underground Mine Emergencies Using a Hybrid CNN-LSTM Model with Data from Delay-Tolerant Network Databases

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines.