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  4. Vol 8, Issue 6, 2024

Research on the prediction of impact ground pressure hazard in deep coal mining based on moving average method

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DOI: 10.23977/acss.2024.080610 | Downloads: 24 | Views: 855

Author(s)

Bingbing Hou 1

Affiliation(s)

1 School of Mathematics and Statistics, Guangxi Normal University, Guilin, China

Corresponding Author

Bingbing Hou

ABSTRACT

As the mining depth of underground increases, the ground stress increases, which inevitably leads to an increase in the probability of impact ground pressure. The hidden danger of impact ground pressure seriously affects the safe and efficient mining of coal mines, so the early warning of impact ground pressure has an important role. In this paper, the identification and prediction of precursor characteristic signals of impact ground pressure are realized by moving average method, decision tree and support vector machine. The data are preprocessed by removing noise signals and normalization, extracting the "Class C" and "non-Class C" features of the preprocessed data, and adjusting the parameters to establish and optimize the interference signal recognition model based on the classification of the feature tree, and applying the model to identify the interference signal and determine the interference signal. The model is used to identify the interfering signals and determine the time interval of the interfering signals. Based on the feature tree classification algorithm of particle swarm optimization, the precursor feature signal identification model is established and applied to identify the precursor feature signals and determine their time intervals, and finally the feature tree algorithm is used to predict and analyze the probability of the appearance of precursor features.

KEYWORDS

Moving Average Method, Decision Tree, Support Vector Machine, Impact Ground pressure

CITE THIS PAPER

Bingbing Hou, Research on the prediction of impact ground pressure hazard in deep coal mining based on moving average method. Advances in Computer, Signals and Systems (2024) Vol. 8: 64-72. DOI: http://dx.doi.org/10.23977/acss.2024.080610.

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