Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural network (RNN) applied to sequential models has motivated groups to pursue RR modeling using RNN. Existing RNN based methods generally use either sequence input single output or unsynced sequence input and output architectures. In this paper, we propose a synced sequence input and output long short-term memory (LSTM) network architecture for hydrologic analysis and compare it to existing methods (sequence input single output LSTM). We expect the model will improve RR prediction in terms of accuracy, calibration training time, and computational cost. The key idea is to efficiently learn the long term dependency of runoff on past rainfall history. To be more specific, we use the indigenous ability of the LSTM network to preserve long term memory instead of artificially setting a time window for input data. In this way, we can avoid losing long term memory of the input, the calibration of the time window length, and excessive computation. The whole procedure mimics the traditional process-driven methods and is closer to the physics interpretation of the RR process. We conducted experiments on real-world hydrologic data from the Brays Bayou in Houston, Texas. Extensive experimental results clearly validate the effectiveness of our proposed method in terms of various statistical and hydrological related evaluation metrics. Notably, our experiment shows that some rainfall events could affect the runoff process in the test watershed for at least a week. For fine temporal resolution prediction, this long term effect needs to be carefully handled, and our proposed method is superior in this case.

准确有效的降雨径流（RR）模拟模型对于洪水风险管理至关重要。最近，将递归神经网络（RNN）应用于顺序模型的成功激发了各团体寻求使用RNN进行RR建模的动力。现有的基于RNN的方法通常使用序列输入单输出或不同步的序列输入和输出体系结构。在本文中，我们提出了一种用于水文分析的同步序列输入和输出长短期记忆（LSTM）网络体系结构，并将其与现有方法（序列输入单输出LSTM）进行比较。我们期望该模型将在准确性，校准训练时间和计算成本方面改善RR预测。关键思想是有效地了解径流对过去降雨历史的长期依赖性。更加具体，我们使用LSTM网络的固有功能来保留长期内存，而不是人为地设置输入数据的时间窗口。这样，我们可以避免丢失输入的长期存储，时间窗口长度的校准以及过多的计算。整个过程模仿了传统的过程驱动方法，并且更接近于RR过程的物理解释。我们对得克萨斯州休斯敦的Brays Bayou的真实水文数据进行了实验。大量的实验结果清楚地证明了我们提出的方法在各种统计和水文相关评估指标方面的有效性。值得注意的是，我们的实验表明，某些降雨事件可能会影响试验流域的径流过程至少一周。对于精细的时间分辨率预测，