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Prediction of coal mine gas emission based on hybrid machine learning model

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Abstract

Coal mine gas accident is one of the most serious threats in the process of safe coal mine mining, making it important to accurately predict coal mine gas emission. To improve the accuracy of coal mine gas emission prediction, a hybrid machine learning prediction model combining random forest (RF) algorithm, improved gray wolf optimizer (IGWO) algorithm and support vector regression (SVR) algorithm is proposed, the model prediction effect is validated by using actual measured gas emission data from a coal mine. Firstly, the RF algorithm is used to screen 13 influencing factors of coal mine gas emission, and finally 6 influencing factors are selected as the input variables of the prediction model; Secondly, the GWO algorithm is improved using the nonlinear convergence factor and DLH search strategy to obtain the IGWO algorithm; Finally, the IGWO algorithm is used to optimize the parameters of the SVR algorithm, and the RF-IGWO-SVR model is established. The results show that the mean absolute percentage error, mean absolute error and root mean square error of the RF-GWO-SVR model are 1.55%, 0.0759, and 0.1103, respectively, and this result is better than the other comparative models, which indicates that the model can effectively improve the prediction accuracy of coal mine gas emission and provide a new model for coal mine gas emission prediction.

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Data Availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

RF:

Random forest

GWO:

Gray wolf optimizer

IGWO:

Improved gray wolf optimizer

SVR:

Support vector regression

RBF:

Radical basis function

DLH:

Dimension learning-based hunting

PCA:

Principal component analysis

ELM:

Extreme learning machine

BP:

Back propagation neural network

MAPE:

Mean absolute percentage error

MAE:

Mean absolute error

RMSE:

Root mean square error

R2 :

Linear regression coefficients of determination

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Funding

Our manuscript is supported by National Natural Science Foundation of China (71771111) and Liaoning Provincial Department of Education Research Funding Project (General Project) (LJKZ0359).

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Authors and Affiliations

  1. Institute of Systems Engineering, Liaoning Technical University, Huludao Liaoning, 125105, China

    Shenghao Bi, Liangshan Shao & Zihan Qi

  2. School of Business Administration, Liaoning Technical University, Huludao Liaoning, 125105, China

    Shenghao Bi, Yanbin Wang & Wenzhe Lai

Authors
  1. Shenghao Bi
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  2. Liangshan Shao
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  3. Zihan Qi
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  4. Yanbin Wang
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  5. Wenzhe Lai
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Contributions

Shenghao Bi: Conceptualization, methodology, data collection & Writing. Liangshan Shao: Review & supervision. Zihan Qi: Writing & language embellishment. Yanbin Wang: Language embellishment & review. Wenzhe Lai: Writing & review. All authors read and approved the fnal manuscript.

Corresponding author

Correspondence to Wenzhe Lai.

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The authors declared that they have no conflicts of interest regarding this work.

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Communicated by H. Babaie

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Bi, S., Shao, L., Qi, Z. et al. Prediction of coal mine gas emission based on hybrid machine learning model. Earth Sci Inform 16, 501–513 (2023). https://doi.org/10.1007/s12145-022-00894-5

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