Resumen
Dam safety is considerably affected by seepage, and uplift pressure is a key indicator of dam seepage. Thus, making accurate predictions of uplift pressure trends can improve dam hazard forecasting. In this study, a convolutional neural network, (CNN)-gated recurrent neural network, (GRU)-based uplift pressure prediction model was developed, which included the CNN model?s feature extractability and the GRU model?s learnability for time series correlation data. Then, the model performance was verified using a dam as an example. The results showed that the mean absolute errors (MAEs) of the CNN-GRU model were 0.1554, 0.0398, 0.2306, and 0.1827, and the root mean square errors (RMSEs) were 0.1903, 0.0548, 0.2916, and 0.2127. The prediction performance was better than that of the particle swarm optimization?back propagation (PSO-BP), artificial bee colony optimization?support vector machines (ABC-SVM), GRU, long short-term memory network (LSTM), and CNN-LSTM models. The method improves the utilization rate of dam safety monitoring results and has engineering utility for safe dam operations.