A Novel Residual Gated Recurrent Unit Framework for Runoff Forecasting

Runoff forecasting is the key to the rational use and protection of water resources by mankind. The large-scale application of machine learning and neural networks in hydrological models has made accurate and reliable short-term runoff forecasting possible. In this article, a novel short-term runoff forecasting framework called ResGRU Plus is proposed with gated recurrent unit (GRU) as the backbone. GRU has the characteristics of long short-term memory (LSTM) that can selectively memorize and forget information while merging gating units to reduce the amount of parameters. Residual network (ResNet) is also deeply integrated with GRU, and its unique shortcut connection effectively solves the degradation problem of traditional neural networks, making it possible to train deep neural networks based on the recurrent architecture. Moreover, a lightweight attention mechanism module: squeeze-and-excitation network (SENet) is embedded in the framework. SENet explicitly models the interdependence between feature dimensions through one global average pooling layer (GAP) and two fully connected (FC) layers, and rescales the original features through the learned weights to adaptively amplify or suppress features. Snapshot ensemble method is also used to train ResGRU Plus, which can integrate multiple homogeneous weak learners through one training process to improve the performance of the model at a small cost. In this article, the hourly runoff of the Columbia River is used as the data set. The Nash-Sutcliffe coefficient (NSE) of Efficiency and coefficient of determination (R-2), which are two common evaluation metrics for hydrological models, are used to measure the performance of the models. Multiple sets of ablation experiments show that the proposed ResGRU Plus, which combines ResNet and the attention mechanism, is able to improve depth by a factor of over 4 and accuracy by nearly 18% compared to the vanilla GRU, which further fully validates the effectiveness of combining the residual structure and attention mechanism with the recurrent architecture-based neural network and the feasibility of applying it to runoff forecasting. In addition, several sets of comparative experiments have also demonstrated the state-of-the-art performance of ResGRU Plus with significant improvement in accuracy compared to mainstream time-series forecasting models.

Automated summary

Runoff forecasting is crucial for the rational use and protection of water resources. This article proposes a novel framework called ResGRU Plus, which combines GRU, ResNet, and SENet to improve the depth and accuracy of the model. Multiple experiments show that ResGRU Plus outperforms traditional models and achieves state-of-the-art performance in runoff forecasting.