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

Xu, Ningke; Wang, Xiangqian; Meng, Xiangrui; Chang, Haoqian

doi:10.3390/s22124412

Cited by 25 publications

(7 citation statements)

References 23 publications

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“…Dey et al [ 25 ] proposed the t-SNE_VAE_bi-LSTM prediction model that combines the t-SNE, VAE, and bi-LSTM networks. Xu et al [ 26 ] constructed a new IWOA-LSTM-CEEMDAN prediction model based on the improved whale optimization algorithm (IWOA). The above methods have improved the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

Peng

Song

Qiu

et al. 2023

Sensors

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In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman’s rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman’s rank correlation coefficient is up to 0.92 at most. The average R2 of predicted value and real value is 0.965, and the average prediction efficiency R for gas abnormal or normal emission is 79.9%. Compared with the other models, the proposed dynamic optimized indicators combined model is more accurate, and the missing alarm of gas abnormal emission is significantly alleviated, which greatly improves the early alarming accuracy. It can assist the safety monitoring personnel in decision making and has certain significance to improve the safety production efficiency of coal mines.

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Section: Introductionmentioning

confidence: 99%

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

Peng

Song

Qiu

et al. 2023

Sensors

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“…The random forest, extreme random regression tree and gradient boosted decision tree (selected GBDT) regression algorithms are used as base learning devices, and the output of each base learner is used as input using a superposition algorithm to train a new model for predicting gas concentrations. The results show that the superposition model had high prediction accuracy [18]. A selfrecurrent wavelet neural network (SRWNN) based on interval prediction rather than point prediction is proposed as a prediction model, which is applicable to the gas concentration prediction system of large-scale intelligent edge devices and is better for gas concentration prediction analysis [19].…”

Section: Introductionmentioning

confidence: 99%

A Spark Streaming-Based Early Warning Model for Gas Concentration Prediction

Huang

Fan

et al. 2023

Processes

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The prediction and early warning efficiency of mine gas concentrations are important for intelligent monitoring of daily gas concentrations in coal mines. It is used as an important means for ensuring the safe and stable operation of coal mines. This study proposes an early warning model for gas concentration prediction involving the Spark Streaming framework (SSF). The model incorporates a particle swarm optimisation algorithm (PSO) and a gated recurrent unit (GRU) model in the SSF, and further experimental analysis is carried out on the basis of optimising the model parameters. The operational efficiency of the model is validated using a control variable approach, and the prediction and warning errors is verified using MAE, RMSE and R2. The results show that the model is able to predict and warn of the gas concentration with high efficiency and high accuracy. It also features fast data processing and fault tolerance, which provides a new idea to continue improving the gas concentration prediction and warning efficiency and some theoretical and technical support for intelligent gas monitoring in coal mines.

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“…To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, Yonghui Xu proposesd a gas concentration prediction algorithm based on a stacking model [3]. Ningke Xu proposed an IWOA-LSTM-CEEMDAN model based on an improved whale optimization algorithm (IWOA) and the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method is used for gas concentration prediction [4]. Yaoyong Xu proposed an improved gravitational search algorithm (IGSA) to train a model that optimizes the initial weights and thresholds of BP neural networks [5].…”

Section: Introductionmentioning

confidence: 99%

“…4) In (3) and (4), HQ,S(qi, sj) is the probability of the joint distribution of HQ (qi) and HS (sj). ( , ) I Q S is a correlation function with respect to the delay time  .…”

mentioning

confidence: 99%

Research on Gas Outburst Prediction Model Based on Multiple Strategy Fusion Improved Snake Optimization Algorithm With Temporal Convolutional Network

Shi

et al. 2022

IEEE Access

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A gas outburst prediction model based on multiple strategy fusion and improved snake optimization algorithm (MFISO) and temporal convolutional network (TCN) is proposed to address the problems of low accuracy of deep learning prediction models for gas outburst in underground mines. By adopting the phase space reconstruction method, the time series of multiple complex influencing factors related to gas outburst were reconstructed and used as model inputs. Sine chaos mapping, spiral search strategy and snake dynamic adaptive weight are introduced to improve the snake optimization algorithm (SO), which enhances the local optimal escape capability and global search capability of the algorithm. The tangent-based rectified linear unit (ThLU) was used to improve the rectified linear unit (ReLU) of the standard TCN to strengthen the generalization capability of the model. The MFISO algorithm was used to optimize the relevant hyperparameters of the improved TCN model to optimize the accuracy of gas outburst prediction. The TCN, GRU, LSTM, SO-TCN, WOA-TCN, and PSO-TCN prediction models were selected to compare the prediction performance with the MFISO-TCN gas outburst prediction model, and the results showed that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MFISO-TCN model were 3.11%, 0.47% and 3.31% are lower than those of other models, which verifies that the method of this paper effectively intensifies the performance of gas outburst prediction model in underground mines.INDEX TERMS Gas outburst, phase space reconstruction method, multiple strategy fusion, snake optimization algorithm, tangent-based rectified linear unit, temporal convolutional network.

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

Cited by 25 publications

References 23 publications

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

A Spark Streaming-Based Early Warning Model for Gas Concentration Prediction

Research on Gas Outburst Prediction Model Based on Multiple Strategy Fusion Improved Snake Optimization Algorithm With Temporal Convolutional Network

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