Prediction of coal mine gas emission based on SHSCOA-BiLSTM model

JIA  Jinzhang; Wang  Yixing; JIA  Peng; Che  Defu

doi:10.6180/jase.202607_30.039

Prediction of coal mine gas emission based on SHSCOA-BiLSTM model

JIA Jinzhang^1,2, Wang Yixing^1,2This email address is being protected from spambots. You need JavaScript enabled to view it., JIA Peng^1,2,3, and Che Defu⁴

¹College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China

²Key Laboratory of Mine Thermodynamic disasters and Control of Ministry of Education, Huludao 125105, China

³Ordos Institute of Liaoning Technical University, Ordos 017010, China

⁴School of Resources and Civil Engineering, Northeastern University

Received: September 24, 2025
Accepted: December 10, 2025
Publication Date: January 25, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202607_30.039

Accurate prediction of coal mine gas emission is crucial for disaster prevention, yet challenging due to complex, non-stationary data and traditional models’ tendency to converge to local optima. The present study proposes a novel SHSCOA-BiLSTM model, which integrates an enhanced chimpanzee optimisation algorithm to optimise a bidirectional long short-term memory network. The methodology employs data imputation, principal component analysis, and enhanced global search strategies to tune critical hyperparameters. The model has been validated on real-world data, and it has been demonstrated to significantly outperform existing benchmarks, with a reduction in mean absolute percentage error of 57.18−74.10% and mean squared error of 80.16−92.35%. The findings indicate that the SHSCOA-BiLSTM model offers a highly accurate and robust instrument for gas emission forecasting, providing a reliable scientific foundation for early warning systems that can significantly enhance proactive safety management and prevent gas-related disasters in coal mines.

Keywords: mine gas outflow prediction, bi-directional long and short-term memory network, chimpanzee optimisation algorithm, hyperparameter optimization, principal component analysis

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