Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm

Wu, Xiang; Yang, Zhen; Wu, Dongrui

doi:10.1155/2021/5551320

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Xiang Wu et al proposed a gas outburst prediction model based on the grey relation analysis (GRA) and adaptive PSO algorithm-optimized SVM. Their study demonstrates that the new model exhibits better performance than the SVM and PSO-SVM outburst prediction models [23].…”

Section: Introductionmentioning

confidence: 98%

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

Tian,

Ma,

et al. 2023

Applied Sciences

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With the rapid advancement of artificial neural network (ANN) algorithms, many researchers have applied these methods to mine gas prediction and achieved numerous research achievements. It is of great significance to study methods that can accurately predict the gas content for the prevention of gas disasters in mining areas. In order to enhance the accuracy, stability, and generalization capability of the gas content prediction model, the GASA-KELM prediction model was established using the GASA algorithm to improve the KELM initial parameter assignment method, and the prediction model based on BPNN and SVM was established under the same conditions. The experimental results show that the GASA-BPNN model failed to achieve the desired outcome within 800 iterations. On the other hand, the GASA-SVM and GASA-KELM models accomplished the goal in significantly fewer iterations, taking only 673 and 487 iterations, respectively. Moreover, the overall average relative errors of the cross-validated gas content predictions were 15.74%, 13.85%, and 9.87% for the three models, respectively. Furthermore, the total average variance of the test set was 3.99, 2.76, and 2.05 for the GASA-BPNN, GASA-SVM, and GASA-KELM models, respectively. As a result, compared with other ANN models, the GASA-KELM model demonstrates higher accuracy, stronger prediction stability, and generalization ability in the practical application. This novel model provides a basis for accurately predicting gas content and proposing effective regional gas management measures.

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Section: Introductionmentioning

confidence: 98%

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

Tian,

Ma,

et al. 2023

Applied Sciences

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“…Among commonly used parameter optimisation algorithms, the particle swarm optimisation (PSO) algorithm has a fast running speed, high prediction accuracy, and is easy to implement, but it is easy to fall into local optimal solutions, so it needs to be improved. Based on the particle swarm optimisation algorithm, the adaptive particle swarm optimisation (APSO) algorithm was proposed by introducing the position factor and the velocity factor [17]. Its operating mechanism is to reinitialise the particles before they fall into the local optimal solution, establish the APSO-TWSVM prediction model, and perform prediction verification and comparative analysis with the field measured data.…”

Section: Introductionmentioning

confidence: 99%

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Du,

Song,

et al. 2024

ELEKTRON ELEKTROTECH

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At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

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“…Liu et al [14] combined a gradient boosting decision tree with the KNN algorithm and proposed an improved classifier model with high prediction accuracy. Support vector machines have evident advantages in predicting small-sample gas outbursts, but they can easily fall into local optima; however, scholars have used the adaptive particle swarm algorithm and Boruta method to optimize it and achieved good prediction results [15][16][17]. Ru et al [18] first proposed the use of correlation coefficients to fill missing data and exception problems, which effectively solved the data problem and provided a new path for similar coal-gas outburst prediction problems.…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

Yang

Fang

Wang

et al. 2022

IJERPH

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Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.

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Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm

Cited by 4 publications

References 14 publications

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm

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