Identification of hidden disaster causing factors in coal mine based on Naive Bayes algorithm

Zhao, Yifan; Tian, Shu

doi:10.3233/jifs-202726

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…[12] In any case, technological innovation and emergence can greatly increase the accuracy of data and effectively reduce the harm caused by mine earthquakes to people. [13] Perhaps, in the near future, this technology can be applied to buildings to ensure more life safety.…”

Section: Discussionmentioning

confidence: 99%

Application of Digital Image Processing Technology in Automatic Recognition Algorithm of Rock Microseismic Events in Mining Area under the Background of Mobile Communication

Wang

Liu

2022

Mobile Information Systems

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In all kinds of production, the changes of the times and scientific and technological progress make us have higher requirements for monitoring data. In order to improve the accuracy of mine rock microseismic monitoring and the sensitivity and specificity of mine disaster early warning, it is very important to use the change of light and material vibration to monitor environmental changes. This paper is the design of an optical hardware system, which improves the traditional system, uses the change of light wave to detect the spatial change, and forms a nano digital imaging photography (ndip) system to collect more detailed data. The three-year field experiment under the guidance of a simulation experiment shows that the system has higher monitoring sensitivity, early warning sensitivity, and specificity than the laser gyroscope system, but its antidust interference ability is low, so it cannot replace the laser gyroscope system in a short time, but it can supplement and realize data fusion analysis.

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Section: Discussionmentioning

confidence: 99%

Application of Digital Image Processing Technology in Automatic Recognition Algorithm of Rock Microseismic Events in Mining Area under the Background of Mobile Communication

Wang

Liu

2022

Mobile Information Systems

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“…A naive Bayes classifier (NBC) and the eight characteristics (above) were used as the main theory for model construction. Since the NBC could not accommodate the missing data for air leakage in gas extraction boreholes, and since the identification and classification accuracy of information with strong correlations is not high, some data easily have a greater impact on the overall model 32 . As shown in Fig.…”

Section: Construction Of An Improved Naive Bayesian Model For the Ide...mentioning

confidence: 99%

Application of an improved naive Bayesian analysis for the identification of air leaks in boreholes in coal mines

Pan

Zhang

et al. 2022

Sci Rep

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Borehole extraction is the basic method used for control of gases in coal mines. The quality of borehole sealing determines the effectiveness of gas extraction, and many influential factors result in different types of borehole leaks. To accurately identify the types of leaks from boreholes, characteristic parameters, such as gas concentration, flow rate and negative pressure, were selected, and new indexes were established to identify leaks. A model based on an improved naive Bayes framework was constructed for the first time in this study, and it was applied to analyse and identify boreholes in the 229 working face of the Xiashijie Coal Mine. Eight features related to single hole sealing sections were taken as parameters, and 144 training samples from 18 groups of real-time monitoring time series data and 96 test samples from 12 groups were selected to verify the accuracy and speed of the model. The results showed that the model eliminated strong correlations between the original characteristic parameters, and it successfully identified the leakage conditions and categories of 12 boreholes. The identification rate of the new model was 98.9%, and its response time was 0.0020 s. Compared with the single naive Bayes algorithm model, the identification rate was 31.8% better, and performance was 55% faster. The model developed in this study fills a gap in the use of algorithms to identify types of leaks in boreholes, provides a theoretical basis and accurate guidance for the evaluation of the quality of the sealing of boreholes and borehole repairs, and supports the improved use of boreholes to extract gases from coal mines.

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“…During rescue, due to the thermal effect existing in the deep coal mines, usually, gas and leaked air can form mixed combustible gases. If the ventilation in the mining area is not good enough, the continuous explosion caused by combustion of coal and gas can worsen the disaster and make the situation more complicated [10][11][12][13][14][15][16]. Moreover, the propagation of shock waves in complex wind networks can damage the ventilation system, once the decision-making misplays, it might expand and deepen the scope and degree of personnel damage [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Identification and Prediction of Thermodynamic Disasters During Deep Coal Mining

Li¹,

Cheng²

2022

IJHT

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Researching the methods to identify and predict thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation, however, existing studies only built static models without evaluating or predicting the development trend of thermodynamic disasters, the research on dynamic modeling methods and rescue decision-making is insufficient, and they generally ignored the mechanism of mutual conversion between fire and gas explosion in deep coal mines. Thus, this paper aims to study the identification and prediction of thermodynamic disasters during deep coal mining. At first, the method for analyzing the thermal field in deep coal mining areas is introduced in detail, and the finite element thermal analysis method is adopted to study the thermodynamic disasters during deep coal mining; then, this paper establishes a thermodynamic disaster prediction model based on the improved Kernel-based Extreme Learning Machine (KELM), and introduces the improved Crow Search Algorithm (CSA) to solve the instability of prediction results caused by artificial selection of model parameters. At last, this paper uses experimental results to verify the validity of the proposed model.

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Identification of hidden disaster causing factors in coal mine based on Naive Bayes algorithm

Cited by 4 publications

References 5 publications

Application of Digital Image Processing Technology in Automatic Recognition Algorithm of Rock Microseismic Events in Mining Area under the Background of Mobile Communication

Application of Digital Image Processing Technology in Automatic Recognition Algorithm of Rock Microseismic Events in Mining Area under the Background of Mobile Communication

Application of an improved naive Bayesian analysis for the identification of air leaks in boreholes in coal mines

Identification and Prediction of Thermodynamic Disasters During Deep Coal Mining

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