Gas concentration prediction based on SSA algorithm with CNN-BiLSTM-attention

Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor ge...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 34214 - 13
Main Authors: Xing, Wenjing, Yang, Yanguo, Zhang, Yanxin, Yang, Yong
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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639/4077
639/705
704/172
Accuracy
Algorithms
Artificial intelligence
Attention mechanism
Coal mines
Concentration prediction
Deep learning
Foraging behavior
Forecasting
Gas concentration
Humanities and Social Sciences
Integrated approach
Machine learning
Mining
multidisciplinary
Neural networks
Occupational safety
Optimization algorithms
Predictions
Science
Science (multidisciplinary)
Sparrow optimization algorithm
Support vector machines
Time series
Wind speed
Deep learning
Sparrow optimization algorithm
Attention mechanism
Concentration prediction
Gas concentration
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Accurate prediction of coal mine gas concentration is a crucial prerequisite for preventing gas exceed and disasters. However, the existing methods still suffer from issues such as low data utilization, difficulty in effectively integrating multivariate nonlinear spatiotemporal features, and poor generalization capability when achieving relatively high prediction accuracy but requiring longer prediction durations. To address these challenges, this study focuses on a tunneling face in a Shanxi coal mine and proposes a novel hybrid deep learning model (CNN-BiLSTM-Attention). The model employs a 1D-CNN to extract local spatial features of gas concentration, temperature, wind speed, rock pressure, and CO concentration, utilizes BiLSTM to model bidirectional temporal dependencies, and incorporates an attention mechanism to dynamically weight critical features, such as sudden changes in gas concentration. Additionally, the sparserow search algorithm (SSA) was applied to automatically optimize hyperparameters, including the number of CNN filters and BiLSTM hidden units. The results demonstrate that the proposed SCBA model achieves an RMSE of 0.0171 and MAPE of 0.084. Compared to mainstream models such as attention-LSTM, SSA-LSTM-aAttention, and rTransformer-LSTM, the RMSE was improved by 23.3%, 4.4%, and 30.2%, respectively, while the MAPE was improved by 38.7%, 14.3%, and 43.62%, respectively, indicating superior prediction accuracy. To validate the contribution of each module, ablation experiments were conducted by sequentially removing the CNN, BiLSTM, and Attention components for error analysis, confirming the necessity of the multi-module collaborative mechanism. In multi-step predictions, the proposed model exhibits better generalization capability as the prediction horizon increases, with the slowest error growth, providing insights for enhancing gas concentration prediction generalization. The model achieves the smallest error at 20 time steps, laying a foundation for subsequent anomaly analysis and offering miners sufficient safety evacuation time, thereby establishing a reliable basis for real-time gas disaster early warning.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-15838-4