Prediction of coal and gas outbursts by a novel model based on multisource information fusion

Li, Yingjie; Yang, Yongguo; Jiang, Bo

doi:10.1177/0144598720913074

Cited by 16 publications

(5 citation statements)

References 56 publications

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“…Digital twins can effectively solve such complex problems, and Wang [19] predicted the coal-gas outburst intensities by conducting experiments based on digital twins with deep learning. Li et al [20] combined traditional methods with artificial intelligence to propose a multisource information fusion model that provided technical support for safe coal mine production. Furthermore, Fisher's prediction methods [21], Bayesian discriminant analysis [22], and simulated annealing genetic algorithms [23,24] have been used.…”

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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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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“…At the same time, some scholars have used machine learning methods to predict gas emissions. The typical mainstream research methods are Dempster-Shafer theory (Li et al 2020), grey correlation analysis (Nie et al 2019), artificial neural network and fuzzy mathematics (Lan et al 2018), the genetic algorithm and projection pursuit method (Liang et al 2017), apriori algorithms (Xie et al 2019). However, most of the above methods are applicable to the mine tunnel, but not in the construction process because of the particularity of traffic tunnel construction.…”

Section: Introductionmentioning

confidence: 99%

Study on BP-LSTM fusion prediction model and application of gas concentration in gas tunnel blasting

Qiu

Xue

et al. 2022

Preprint

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With the development of China's transportation industry, more and more tunnel projects will pass through the gas geological area, resulting in the tunnel construction will face a huge hidden danger. The gas will flow out rapidly after the excavation of the working face. The distribution and concentration of gas in the tunnel are the key factors affecting the gas disaster. In time, grasping the gas concentration near the excavation face is the key to avoiding safety accidents. Therefore, predicting the gas concentration near the tunnel excavation face is necessary after blasting excavation. This paper relies on the NEW Chengdu-Kunming Railway Like Tunnel project to conduct research. According to the collected historical gas concentration data, based on the back-propagation neural network and long and short-term memory neural network, a new gas concentration prediction model based on the fusion algorithm of back-propagation and long and short-term memory (BP-LSTM) is proposed to realize the long-term prediction of gas concentration near the face of the tunnel after blasting excavation. This paper used the Pearson correlation method to improve model prediction accuracy and analyze the correlation between geological parameters and environmental factors and gas concentration occurrence. This model predicts the gas concentration of four monitoring points (LAFEF monitoring point, RAFEF monitoring point, EFV monitoring point, and AFEF monitoring point) in actual tunnel engineering. And compared with the prediction results of the back-propagation and long and short-term memory models, the model was evaluated according to the error. The results indicate that the prediction result of this model has high accuracy, and it can be successfully applied to predict the gas concentration after excavating the face of the gas tunnel, which can ensure the safety of construction.

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“…At present, some researchers have made good research progress in using artificial intelligence methods to predict gas outburst. Yingjie Li et al established a coupled forecasting model and achieved good forecasting results by combining Dempster-Shafer theory and traditional forecasting methods [8]. Dong Chunyou and others proposed G-K evaluation and a rough set model to analyze and classify gas outburst grades [9]; Cheng Xia and Xu Manguan analyzed the factors affecting gas outburst based on the gray system theory and selected the main control factors to simplify the prediction process and increase the prediction speed [10,11].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

2021

Shock and Vibration

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Gas outburst poses a huge threat to the safe production of coal mines. Therefore, the prediction of gas outburst has always been a hot topic for researchers. In recent years, the use of artificial intelligence algorithms for gas outburst prediction has made progress, such as using BP neural network, GA algorithm, and SVM algorithm. Despite these progresses, predicting the gas outburst more accurately and efficiently still remains a great challenge. In this work, an algorithm based on grey relational analysis and SVM using adaptive particle swarm optimization (APSO-SVM) for gas outburst prediction is proposed. Grey relational analysis was used to extract the four most relevant ones from nine gas outburst prediction parameters (geological structure zone distance, coal seam gas content, gas release initial velocity, gas desorption index-K1, drill cuttings volume, coal seam depth, coal seam thickness, coal destruction type, and coal firmness coefficient) to give the needed parameters for SVM model. Higher prediction accuracy was then obtained with the selected parameters composed by coal seam gas content, gas release initial velocity, gas desorption index-K1, and drill cuttings volume. Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. The APSO-SVM model was applied to the prediction of gas outburst in 31004 tunneling face of Xinyuan Coal Mine, Yangquan City, Shanxi Province, China. We further introduced the criteria of accuracy, precision, recall, and F 2 -score to evaluate the prediction results of different models. The results show that, in the gas outburst prediction, the accuracy of the APSO-SVM model is 98.38%, the precision and recall are both 100%, and F 2 -score is 1. Comparative studies confirm that APSO-SVM displayed better performance than SVM and PSO-SVM models for the applied grey relational analysis assisted gas outburst prediction. These obtained results indicate the validity of APSO-SVM model for gas outburst prediction.

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Prediction of coal and gas outbursts by a novel model based on multisource information fusion

Cited by 16 publications

References 56 publications

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

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

Study on BP-LSTM fusion prediction model and application of gas concentration in gas tunnel blasting

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

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