基于贝叶斯分类器的顶煤可放性预测研究  

Prediction of Top Coal Caving Ability Based on Bayesian Classifier

作　　者：徐旖旎 石永奎 齐敏华 

机构地区：[1]山东科技大学矿业与安全工程学院,山东青岛266590

出　　处：《煤炭技术》2018年第1期99-101,共3页Coal Technology

摘　　要：顶煤可放性预测是放顶煤开采合理应用的关键技术。以29个放顶煤工作面样本数据为例,通过粗糙集理论对数据进行处理。将开采深度、煤体强度、煤体裂隙分布、夹矸比和煤层厚度作为5个输入变量,建立贝叶斯分类器模型,对其中7个放顶煤工作面的顶煤可放性进行预测。预测精度能够达到85.71%,预测模型可以在工程中推广运用。The prediction of top coal caving ability is the key technology for rational mining. With 29 samples data, rapid and accurate prediction of top coal caving ability level, those data are applied by rough set theory. Mining depth, coal seam strength, coal seam fracture, gangue ratio and thickness are as 5 input variables to establish Bayesian classifier model for predicting top coal caving abilities of 7 fully mechanized caving faces. The prediction accuracy can reach 85.71%, and the prediction model can be applied in engineering.

关 键 词：放顶煤开采 贝叶斯分类器 顶煤可放性 

分 类 号：TD823.4[矿业工程—煤矿开采]