Early Warning Method for Coal and Gas Outburst Prediction Based on Indexes of Deep Learning Model and Statistical Model

Chong, Wang; Wei, Like; Hu, Haiyong; Wang, Jiren; Jiang, Mengfeng

doi:10.3389/feart.2022.811978

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Each disaster system has its unique construction, attributes, and environment. From a structural perspective, different types of elements and disaster accidents collectively constitute the entire disaster system, and disasters with similar attributes form their respective smaller sub-systems within the disaster system [21][22][23]. Simultaneously, this study provides the following explanation for the risk of gas explosion disasters: There are complex relationships and interactions among the sub-systems of causative factors, the environment conducive to disaster occurrence, and the vulnerable elements within the coal mine disaster system.…”

Section: Plos Onementioning

confidence: 90%

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

Li,

Zhang,

Yang

2023

PLoS ONE

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In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

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Section: Plos Onementioning

confidence: 90%

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

Li,

Zhang,

Yang

2023

PLoS ONE

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“…Step 9: Update the position of vigilantes aware of danger according to Equation (22). Sparrows at the periphery of the population will move closer to the safe area.…”

Section: Conduct Group Experiments To Select the Best Parameter Indic...mentioning

confidence: 99%

“…Yuhong Wang [20] used gray correlation analysis to screen coal and gas prominence influencing factors for dimensionality reduc-tion and established particle swarm optimization algorithms based on sub-dimensional evolution and quantum gate node neural network models for prediction experiments. Kai Wang [21,22] et al explored the mechanism of coal and gas protrusion, analyzed typical cases, and used big data and deep learning techniques to achieve coal and gas protrusion prediction. Wang Wei [23] constructed a prediction model after determining the subjective and objective weights of the predictors and conducting experiments on hazard prediction in mines.…”

Section: Introductionmentioning

confidence: 99%

A Gas Prominence Prediction Model Based on Entropy-Weighted Gray Correlation and MCMC-ISSA-SVM

Shao

Gao

2023

Processes

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To improve the accuracy of coal and gas prominence prediction, an improved sparrow search algorithm (ISSA) and an optimized support vector machine (SVM) based on the Markov chain Monte Carlo (MCMC) filling algorithm prediction model were proposed. The mean value of the data after filling in the missing values in the coal and gas prominence data using the MCMC filling algorithm was 2.282, with a standard deviation of 0.193. Compared with the mean fill method (Mean), random forest filling method (random forest, RF), and K-nearest neighbor filling method (K-nearest neighbor, KNN), the MCMC filling algorithm showed the best results. The parameter indicators of the salient data were ranked by entropy-weighted gray correlation analysis, and the salient prediction experiments were divided into four groups with different numbers of parameter indicators according to the entropy-weighted gray correlation. The best results were obtained in the fourth group, with a maximum relative error (maximum relative error, REmax) of 0.500, an average relative error (average relative error, MRE) of 0.042, a root mean square error (root mean square error, RMSE) of 0.144, and a coefficient of determination (coefficient of determination, R2) of 0.993. The best predicted parameters were the initial velocity of gas dispersion (X2), gas content (X4), K1 gas desorption (X5), and drill chip volume (X6). To improve the sparrow search algorithm (sparrow search algorithm, SSA), the adaptive t-distribution variation operator was introduced to obtain ISSA, and the prediction models of improved sparrow search algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-ISSA-SVM), sparrow search algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-SSA-SVM), genetic algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC-GA-SVM) and particle swarm optimization algorithm optimized support vector machine based on Markov chain Monte Carlo filling algorithm (MCMC- PSO -SVM) were established for coal and gas prominence prediction using the ISSA, SSA, genetic algorithm (genetic algorithm, GA) and particle swarm optimization algorithm (particle swarm optimization, PSO) respectively. Comparing the prediction experimental results of each model, the prediction accuracy of MCMC-ISSA-SVM is 98.25%, the error is 0.018, the convergence speed is the fastest, the number of iterations is the least, and the best fitness and the average fitness are the highest among the four models. All the prediction results of MCMC-ISSA-SVM are significantly better than the other three models, which indicates that the algorithm improvement is effective. ISSA outperformed SSA, PSO, and GA, and the MCMC-ISSA-SVM model was able to significantly improve the prediction accuracy and effectively enhance the generalization ability.

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“…With the development of intelligent coal mines, accurate and efficient data mining has become an inevitable prerequisite for intelligent analysis and decision making, including the analysis and mining of gas concentration data. In recent years, as coal mining operations continue to expand much deeper, the underground operating environment has become increasingly complex, and gas hazards have become increasingly prominent [6][7][8][9]. At this stage, gas data monitoring technology has gradually matured, realising real-time online monitoring and real-time transmission and enabling online analysis and prediction based on various prediction models.…”

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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Early Warning Method for Coal and Gas Outburst Prediction Based on Indexes of Deep Learning Model and Statistical Model

Cited by 10 publications

References 24 publications

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

A Gas Prominence Prediction Model Based on Entropy-Weighted Gray Correlation and MCMC-ISSA-SVM

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

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