Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system

IoT-enabled sensor devices and machine learning methods have played an essential role in monitoring and forecasting mine hazards. In this paper, a prediction model has been proposed for improving the safety and productivity of underground coal mines using a hybrid CNN-LSTM model and IoT-enabled sensors. The hybrid CNN-LSTM model can extract spatial and temporal features from mine data and efficiently predict different mine hazards. The proposed model also improves the flexibility, scalability, and coverage area of a mine monitoring system to an underground mine's remote locations to minimize the loss of miners' lives. The proposed model efficiently predicts miner's health quality index (MHQI) for working faces and gases in goaf areas of mines. The experimental results demonstrated that the predicted mean square error of the proposed model is less than 0.0009 and 0.0025 for MHQI; 0.0011 and 0.0033 for CH4 in comparison with CNN and LSTM models, respectively. The less means square error indicates the better prediction accuracy of the trained. Similarly, the correlation coefficient (R2) value of the proposed model is found greater than 0.005 and 0.001 for MHQI; 0.007 and 0.001 for CH4 compared to CNN and LSTM models, respectively. Thus, the proposed CNN-LSTM model performed better than the two existing models.