Analysis of Comprehensive Prediction Method for Coal Mine Impact Hazards Based on Machine Learning
Aiming at the difficult problem of hazard prediction of rock burst in coal mine,a new type of prediction model—Dynamic Fuzzy Inference Neural Network(DFINN),based on the combination of fuzzy logic and neural network technology,is proposed.Fuzzy mathematics and fuzzy neural network technology are used to process complex information about rock burst hazards in coal and rock masses.The DFINN model can adaptively learn and integrate the complexity of geological conditions and the dynamics of mining activities,achieving accurate prediction of rock burst hazard levels.This model can effectively identify the hazard levels of strong impact,medium impact,and weak impact,improve the efficiency and quality of coal mine impact hazard prediction,and provide effective decision support for mine safety management.Through verification of 128 sets of data samples,the model shows good accuracy and reliability in predicting strong and weak impact hazards,and maintains high robustness even under condictions of small sample sizes and incomplete data.
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