基于PCA与权重贝叶斯的工作面煤与瓦斯突出预测

为实现对工作面煤与瓦斯突出快速、准确和动态的预测，提出一种基于主成分分析和权重贝叶斯的工作面煤与瓦斯突出预测方法，通过建立工作面煤与瓦斯突出预测的权重贝叶斯模型进行突出危险性等级预测。利用主成分分析确定预测模型中分类变量权重以提高预测准确性。在此基础上，设计基于相似度的训练样本数据更新方式实现对突出预测模型的有效重构。实验结果表明，与朴素贝叶斯模型和权重贝叶斯模型相比，基于主成分分析和权重贝叶斯工作面煤与瓦斯突出预测方法能快速获得高准确度的突出预测结果，为现场指导矿井工作面安全生产提供参考。

Prediction of Coal and Gas Outburst in Mine Working Face Based on PCA and Weighted Bayesian

In order to realize fast, accurate and dynamic prediction of coal and gas outburst in working face, a prediction method based on Principal Component Analysis (PCA) and the weighted Bayesian model is proposed. A weighted Bayesian model for coal and gas outburst prediction in mine working face is built, which also determines the class of the outburst risk. Then the weight of classified variables in the prediction model is determined by using PCA to improve the accuracy of prediction. On this basis, the updating mechanism of the training data is designed based on similarity to rebuild the prediction model effectively. The experimental results show that compared with the naïve Bayesian model and the weighted Bayesian model, the proposed method could quickly generate more accurate prediction results, providing reference for on-site direction of production in mine working face.