Accurate streamflow forecasting has always been a challenge. Although there were many novel approaches using deep learning models, accuracy of these models is often limited to a short lead time. This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 h. We use a semi-distributed model structure with observation and forecast data from the model output of upstream stations as additional input for downstream gages. The proposed model outperforms the streamflow persistence, ridge regression and random forest regression on majority of the gages. Our model has shown strong predictive power and can be used for long-term streamflow predictions. This study also shows that the semi-distributed structure in NRM can improve the streamflow predictions by integrating water level data from upstream stream gauges.

准确的流量预测一直是一个挑战。尽管有许多使用深度学习模型的新颖方法，但这些模型的准确性通常仅限于较短的交货时间。这项研究提出了一种新的深度递归神经网络方法，即神经径流模型（NRM），该方法已应用于爱荷华州的125个USGS流量测量仪中，用于预测接下来的120小时。我们使用半分布式模型结构，将上游站的模型输出中的观测和预测数据作为下游量具的附加输入。所提出的模型在大多数量具上均优于流径持久性，岭回归和随机森林回归。我们的模型显示出强大的预测能力，可用于长期流量预测。