Prediction of the height of a water-flowing fracture zone (WFFZ) is the foundation for evaluating water bursting conditions on roof coal. By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness, burial depth, working face length, and roof category on the height of a WFFZ, we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height. Based on data of WFFZ height and its influence index obtained from field observations, a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine. The reliability and superiority of the prediction model were verified by a comparative study and an engineering application. The results show that the main factors affecting WFFZ height in the study area are coal seam thickness, burial depth, working face length, and roof category. Compared with multiple-linear-regression and back-propagation neural-network approaches, the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.

预测流水裂隙带（WFFZ）的高度是评估顶板煤突水条件的基础。以宾昌矿区为研究区域，深入研究煤层厚度，埋深，工作面长度和顶板类别对WFFZ高度的影响，提出硬岩比例应该使用不同屋顶范围的屋顶来表征屋顶类别对WFFZ高度的影响。基于WFFZ高度的数据及其从野外观察获得的影响指数，结合遗传算法和支持向量机，建立了WFFZ高度的预测模型。通过比较研究和工程应用验证了预测模型的可靠性和优越性。结果表明，影响研究区WFFZ高度的主要因素是煤层厚度，埋深，工作面长度和顶板类别。与多元线性回归和反向传播神经网络方法相比，基于遗传算法支持向量机的WFFZ高度预测模型具有更高的训练和预测精度，更适合于WFFZ的预测。矿区。