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. 2022 Jun 10;22(12):4412.
doi: 10.3390/s22124412.

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

Ningke Xu  1   2 , Xiangqian Wang  3 , Xiangrui Meng  1   3 , Haoqian Chang  3
Affiliations

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

Ningke Xu et al. Sensors (Basel). .

Abstract

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm's (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

Keywords: CEEMDAN decomposition and reconstruction; LSTM; coal mine safety; gas concentration prediction; whale optimisation algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
LSTM structure diagram.
Figure 2
Figure 2
CEEMDAN decomposition flow chart.
Figure 3
Figure 3
IWOA-LSTM-CEEMDAN residual correction model prediction flow chart.
Figure 4
Figure 4
Line chart of cumulative contribution of parameters.
Figure 5
Figure 5
Convergence curves of each test function. (a) Comparison of the two algorithms on f1; (b) Comparison of the two algorithms on f2; (c) Comparison of the two algorithms on f3; (d) Comparison of the two algorithms on f4; (e) Comparison of the two algorithms on f5; (f) Comparison of the two algorithms on f6; (g) Comparison of the two algorithms on f7; (h) Comparison of the two algorithms on f8; (i) Comparison of the two algorithms on f9; (j) Comparison of the two algorithms on f10.
Figure 5
Figure 5
Convergence curves of each test function. (a) Comparison of the two algorithms on f1; (b) Comparison of the two algorithms on f2; (c) Comparison of the two algorithms on f3; (d) Comparison of the two algorithms on f4; (e) Comparison of the two algorithms on f5; (f) Comparison of the two algorithms on f6; (g) Comparison of the two algorithms on f7; (h) Comparison of the two algorithms on f8; (i) Comparison of the two algorithms on f9; (j) Comparison of the two algorithms on f10.
Figure 6
Figure 6
CEEMDAN decomposition of residual sequences.
Figure 7
Figure 7
Predicted and actual results for each model.
Figure 7
Figure 7
Predicted and actual results for each model.
Figure 8
Figure 8
Case study of coal and gas outburst accident.
Figure 9
Figure 9
Model evaluation indicators for different coal mines.
See this image and copyright information in PMC

References

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