中文摘要：

为确定合理的底板防水煤岩柱尺寸,减少底板突水安全事故的发生,利用支持向量机（SVM）与人工蜂群算法（ABCA）综合研究底板破坏深度问题。由于SVM训练参数惩罚因子C和核函数宽度g的选择对预测精度的影响显著,采用ABCA优化该训练参数的选择过程,建立基于SVM的底板破坏深度预测模型。选取采深、煤层倾角、采厚、工作面斜长、底板抗破坏能力和是否有切穿断层或破碎带作为影响底板破坏深度的主要影响指标,利用现场实测的30组数据作为样本对该模型进行训练和预测。结果表明：该预测模型的平均相对误差为12.5%,平均绝对误差为0.986m,均方误差为0.005,平方相关系数为0.980,较其他预测模型具有更强的泛化能力和更高的预测精度。

英文摘要：

To determine a reasonable size of floor waterproof coal pillar and reduce floor water invasion incidents,support vector machine（SVM）and artificial bee colony algorithm（ABCA）are used to research floor failure depth problem.Prediction accuracy is significantly affected by SVM training parameter choice of penalty factor Cand kernel width g.ABCA is used to optimize the selection of training parameters,and a floor failure depth prediction model is established based on SVM.Selecting mining depth,coal seam dip,mining thickness,face plagioclase,anti-destruction capability of floor and whether there is fault fracture zone cutting through the floor as major impact indicators of floor damaged depth,we use 30 sets of measured data for training model and forecasting.The results show that average relative error is 12.5%,average absolute error is 0.986 m,mean square error is 0.005,and squared correlation coefficient is 0.980.This prediction model has stronger generalization ability and higher prediction accuracy compared with other prediction models.