The prediction of damage degree of coal floor based on the Naive Bayes Classifier

Considering the complexity of floor damage in coal mining, based on the summary of measured data of typical damage degree of coal floor nation-wide in China, this paper figures out that damage degree of coal floor is closely linked to six major factors, including depth of coal seam, dip angle of coal seam, thickness of coal seam, dip length of coal face, anti-damage ability of the floor and fractured zone. The author applies rough set theory, Bayesian theory, and theory of Nonlinear Subjects, through Excel, Weka and UltraEdit software, to constructing prediction model based on Naive Bayes Classifier, making the prediction of damage degree of floor accurate. By training and predicting samples, the conclusion was found that prediction with high accuracy based on Naive Bayes Classifier is not only supported by reliable theory, but of relatively practical application value on the spot.