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首页> 外文会议>International symposium on risk control and managment of design, construction and operation in underground engineering >A Bayes Discriminant Analysis Method for Predicting the Hazard Classification of Rockburst and its Application
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A Bayes Discriminant Analysis Method for Predicting the Hazard Classification of Rockburst and its Application

机译：一种矛盾判别分析方法，用于预测岩爆危害分类及其应用

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摘要

A method to forecast the hazard classification of rockburst by using the Bayes discriminant analysis theory is presented in this paper. The Bayes discriminant analysis (BDA) theory was introduced firstly. Then considering the mining circumstances and geological conditions of rockburst, eight factores reflecting the rockburst, including exploitation depth of coal seam, lithology of stope roof, complicated degree of conformation, dip angel of coal seam, thickness of coal seam, exploitation method, coal pole state and blasting mining/comprehensive mining, were considered and 14 specific indexes were selected to establish a BDA model. 24 samples of the Zhangji Mine in Xuzhou City of China were used as the training and forecasting samples. The prior probability of each collectivity was obtained according to the ratio of training samples and re-substitution method was also introduced to verify the stability of model. Compared with the artificial neural network (ANN) method and support vector machine (SVM) method, the results show that this BDA model has excellent performance, high prediction accuracy and can be used in practical engineering.
机译：本文介绍了通过使用贝叶斯判别分析理论来预测岩爆危害分类的方法。首先介绍了贝叶斯判别分析（BDA）理论。然后考虑到摇滚爆发的采矿环境和地质条件，反映岩爆的八个事实，包括煤层的开发深度，岩岩岩性，构造的复杂程度，煤层浸煤层，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度，煤层厚度陈述和爆破采矿/综合采矿，选择了14项具体指标以建立BDA模型。 24中国徐州市张吉矿样品被用作培训和预测样品。根据训练样品的比率和再替换方法的比例，还获得了每种集合的现有概率，以验证模型的稳定性。与人工神经网络（ANN）方法和支持向量机（SVM）方法相比，结果表明，该BDA模型具有出色的性能，高预测精度，可用于实际工程。

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