Accurate daily runoff prediction plays an important role in the management and utilization of water resources. In order to improve the accuracy of prediction, this paper proposes a deep neural network (CAGANet) composed of a convolutional layer, an attention mechanism, a gated recurrent unit (GRU) neural network, and an autoregressive (AR) model. Given that the daily runoff sequence is abrupt and unstable, it is difficult for a single model and combined model to obtain high-precision daily runoff predictions directly. Therefore, this paper uses a linear interpolation method to enhance the stability of hydrological data and apply the augmented data to the CAGANet model, the support vector machine (SVM) model, the long short-term memory (LSTM) neural network and the attention-mechanism-based LSTM model (AM-LSTM). The comparison results show that among the four models based on data augmentation, the CAGANet model proposed in this paper has the best prediction accuracy. Its Nash–Sutcliffe efficiency can reach 0.993. Therefore, the CAGANet model based on data augmentation is a feasible daily runoff forecasting scheme.

准确的每日径流预报在水资源的管理和利用中起着重要作用。为了提高预测的准确性，提出了由卷积层，注意力机制，门控递归单元（GRU）神经网络和自回归（AR）模型组成的深度神经网络（CAGANet）。鉴于每日径流序列是突变且不稳定的，因此单个模型和组合模型很难直接获得高精度的每日径流预测。因此，本文采用线性插值方法来增强水文数据的稳定性，并将增强后的数据应用于CAGANet模型，支持向量机（SVM）模型，长期短期记忆（LSTM）神经网络和关注-基于机制的LSTM模型（AM-LSTM）。比较结果表明，在四个基于数据扩充的模型中，本文提出的CAGANet模型具有最佳的预测精度。它的纳什-苏特克利夫效率可以达到0.993。因此，基于数据扩充的CAGANet模型是一种可行的每日径流预报方案。