Gas Concentration Prediction in Underground Coal Mining
Summary
Accurate prediction of gas concentrations in underground coal mines is critical for preventing hazards such as explosions, outbursts and asphyxiation. The complex interplay of geological structures, mining advancement and ventilation regimes gives rise to highly non-stationary and nonlinear gas emission patterns. Traditional empirical and physical models often struggle with site-specific variability, while purely statistical approaches can be limited by data sparsity and signal noise. Recent advances in sensor networks, data assimilation and machine-learning algorithms have enabled time-series forecasting models that learn directly from real-time measurements. Deep neural networks, ensemble methods and hybrid frameworks now offer the potential to capture both short-term fluctuations and long-term trends, supporting dynamic risk assessment and adaptive ventilation control. Integrating these predictive tools into mine-wide information systems strengthens early-warning capabilities and informs targeted gas drainage strategies, thereby enhancing safety and operational efficiency on a global scale.
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Gas Concentration Prediction in Underground Coal Mining publication trend
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Technical terms
Long short-term memory (LSTM): A recurrent neural network architecture with gated memory cells that retain information over extended sequences, well suited for time-series prediction.
Improved whale optimisation algorithm (IWOA): An enhanced swarm-intelligence optimizer that refines search diversity and convergence speed for model hyperparameter tuning.
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN): A data-driven signal decomposition technique that extracts intrinsic mode functions from non-stationary time series to isolate and model residual components.
LightGBM: A high-performance gradient-boosting framework that constructs decision-tree ensembles with efficient handling of large datasets and complex feature interactions.
References
- A software for calculating coal mine gas emission quantity based on the different-source forecast method. International Journal of Coal Science & Technology (2024).
- Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model. Sensors (2022).
- Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model. Energies (2022).
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