{"id":869,"date":"2026-03-08T01:12:35","date_gmt":"2026-03-07T17:12:35","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=869"},"modified":"2026-04-12T01:00:20","modified_gmt":"2026-04-11T17:00:20","slug":"prediction-of-coal-mine-gas-emission-based-on-shscoabilstm-model","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=prediction-of-coal-mine-gas-emission-based-on-shscoabilstm-model","title":{"rendered":"Prediction of coal mine gas emission based on SHSCOABiLSTM model"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 30<\/a><\/p>\n<\/div>\n\n\n\n

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JIA Jinzhang1,2<\/sup>, Wang Yixing1,2<\/sup><\/i><\/a>, JIA Peng1,2,3<\/sup>, and Che Defu4<\/sup><\/p>\n\n\n\n

1<\/sup>College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China<\/p>\n\n\n\n

2<\/sup>Key Laboratory of Mine Thermodynamic disasters and Control of Ministry of Education, Huludao 125105, China<\/p>\n\n\n\n

3<\/sup>Ordos Institute of Liaoning Technical University, Ordos 017010, China<\/p>\n\n\n\n

4<\/sup>School of Resources and Civil Engineering, Northeastern University<\/p>\n<\/div>\n\n\n\n

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Received: September 24, 2025
Accepted: December 10, 2025
Publication Date: March 8, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Update COA Location<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:  RIS<\/a> | BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202607_30.039<\/a>  <\/p>\n\n\n\n

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Accurate prediction of coal mine gas emission is crucial for disaster prevention, yet challenging due to complex, non-stationary data and traditional models\u2019 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 \u2212 74.10% and mean squared error of 80.16 \u2212 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.<\/p>\n\n\n\n

Keywords: mine gas outflow prediction, bi-directional long and short-term memory network, chimpanzee optimisation algorithm, hyperparameter optimization, principal component analysis<\/em><\/p>\n\n\n\n

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