Spectroscopy and Spectral Analysis, Volume. 42, Issue 10, 3091(2022)

Research on Mine Water Inrush Identification Based on LIF and LSTM Neural Network

Peng-cheng YAN1、*, Xiao-fei ZHANG2、2; *;, Song-hang SHANG2、2;, and Chao-yin ZHANG2、2;
Author Affiliations
  • 11. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
  • 22. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
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    Figures & Tables(6)
    Original spectra and spectra pretreated by using different methods
    Dimension reduction results of LDA under different pretreatments
    Recognition results of test sets in different models
    The changing trends of accuracy in training process of different models
    The changing trends of loss function in training process of different models
    • Table 1. Accuracies of test set in different models

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      Table 1. Accuracies of test set in different models

      预处理方法降维测试集准确率
      OriginalLDA100%(70/70)
      MinMaxScaler98.57%(69/70)
      SG100%(70/70)
      SNV87.14%(61/70)
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    Peng-cheng YAN, Xiao-fei ZHANG, Song-hang SHANG, Chao-yin ZHANG. Research on Mine Water Inrush Identification Based on LIF and LSTM Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3091

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    Paper Information

    Category: Research Articles

    Received: Aug. 4, 2021

    Accepted: Nov. 5, 2021

    Published Online: Nov. 23, 2022

    The Author Email: Peng-cheng YAN (PCYan1988@126.com)

    DOI:10.3964/j.issn.1000-0593(2022)10-3091-06

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