During the construction of a tunnel, water inflow is one of the most common and complex geological disasters and has a large impact on the construction schedule and safety. When serious water inflows occur in tunnel construction, huge economic losses and casualties can occur. Therefore, this phenomenon's prediction is an important task to ensure the safety and schedule during the underground construction process. In this article, water inflow into tunnels was predicted using six machine learning techniques of long short-term memory (LSTM), deep neural networks (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) by applying 600 datasets. The key features of the models mentioned above were discussed. Finally, in terms of accuracy, the models were ordered as LSTM, DNN, GPR, SVR, KNN, and DT with the route mean squared errors of 4.07486, 4.66526, 5.77216, 12.95589, 16.63670, and 17.99058, respectively.

在隧道施工过程中，进水是最常见，最复杂的地质灾害之一，对施工进度和安全影响很大。如果在隧道施工中出现严重的水流入，可能会造成巨大的经济损失和人员伤亡。因此，对该现象的预测是确保地下施工过程安全性和进度的重要任务。在本文中，使用长短期记忆（LSTM），深度神经网络（DNN），K近邻（KNN），高斯过程回归（GPR），支持向量回归等六种机器学习技术预测了隧道的入水量（SVR）和决策树（DT），方法是应用600个数据集。讨论了上述模型的关键特征。最后，在准确性方面，这些模型按LSTM，DNN，GPR，