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Review on Machine Learning-Based Underground Coal Mines Gas Hazard Identification and Estimation Techniques

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Abstract

The underground coal mines (UCM) exhibit many life-threatening hazards for mining workers. In contrast, gas hazards are among the most critical challenges to handle. This study presents a comparative study of the sensor fusion methodologies related to UCM gas hazard prediction and classification. The study provides a brief theoretical background of the existing methodologies and their usage to mitigate the gas hazard issues in UCM. A brief comparison report emphasising the advantages and disadvantages of the existing models related to the UCM gas hazard monitoring is presented. Additionally, a separate comparison is also drawn, considering only neural network models based on their prediction accuracy and other performance metrics. This study attempts to observe and compare the Neural network models with the conventional method in the field of UCM gas hazard prediction, which is not explored in this fraternity.

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  1. Mayank Sharma and Tanmoy Maity have contributed equally to this work.

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  1. Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhnad, 826004, India

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Sharma, M., Maity, T. Review on Machine Learning-Based Underground Coal Mines Gas Hazard Identification and Estimation Techniques. Arch Computat Methods Eng 31, 371–388 (2024). https://doi.org/10.1007/s11831-023-09982-1

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