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Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+FICA–LDA and optimized QPSO-DELM classifier

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Abstract

Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary; the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, discrete wavelet transform (DWT) is utilized as a preprocessing technique to decompose subseries and extract the features with different frequencies and the optimal feature components are retained; second, in order to eliminate the redundancy between the features and uncorrelation between features and outbursts, we use the fast independent component analysis (FICA) to obtain each independent component, obtaining the global information in the feature; then, the obtained features are input into linear discriminant analysis (LDA), under the guidance of class labels, then the local information in features is obtained; finally, the projected features are input into the deep extreme learning machine (DELM) classifier based on the optimal parameters by quantum particle swarm optimization (QPSO) for training and classification. The experimental results on the dataset of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators.

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Availability of data and material

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (U1704242); the authors of this paper would like to thank the insightful and valuable comments from Hebei Key Laboratory of IOT blockchain integration.

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Authors and Affiliations

  1. School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing, P. R. China

    Xuning Liu, Zixian Zhang & Guoying Zhang

  2. Department of Computer Engineering, Shijiazhuang University, Shijiazhuang, P. R. China

    Xuning Liu & Zhixiang Li

  3. School of Foreign University, Liaocheng University, Liaocheng, P. R. China

    Zixian Zhang

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  1. Xuning Liu
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  2. Zhixiang Li
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  4. Guoying Zhang
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Correspondence to Zixian Zhang.

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Liu, X., Li, Z., Zhang, Z. et al. Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+FICA–LDA and optimized QPSO-DELM classifier. J Supercomput 78, 2909–2936 (2022). https://doi.org/10.1007/s11227-021-03964-5

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