随着计算机技术的发展，机器学习方法已成为煤层底板突水预测的重要技术；算法预测精准度对样本的数量要求较高，制约着实际应用。运用最近邻算法（KNN）以及梯度提升决策树（GBDT）与逻辑回归（LR）结合运用的算法，基于以水压、采高、隔水层厚度、断层落差、煤层倾角、断层距工作面距离等6项指标的样本数据建立了突水预测模型，讨论了样本数量对预测精度的影响规律，并与常用的粒子群、支持向量机、BP神经网络、随机森林以及卷积神经网络进行对比研究。研究结果表明：当样本数量达到18时，KNN和GBDT+LR预测精度保持稳定；KNN与GBDT+LR在小样本条件下的预测精度高于常规预测模型；模型预测结果与实际情况相符。

With the development of computer technology, machine learning method has become an important technology for the prediction of water inrush in coal seam floor. However, the prediction accuracy of many machine learning algorithms requires a high number of samples, which restricts the practical application. In this paper, by using the nearest neighbor algorithm (KNN) and the combination algorithm of gradient boosting decision tree (GBDT) and logistic regression (LR), a water inrush prediction model was established based on the sample data of six indexes, including water pressure, mining height, water-barrier thickness, fault drop, coal seam inclination, and fault distance from the working face. The influence rule of sample number on prediction accuracy was discussed, and the comparison study was conducted with the commonly used particle swarm, support vector machine, BP neural network, random forest and convolutional neural network. The results show that when the number of samples reaches 18, the prediction accuracy of KNN and GBDT+LR remains stable. The prediction accuracy of KNN and GBDT+LR is higher than that of conventional models under small sample conditions. The predicted results of the model agree with the actual situation.