Utilizing Hybrid Deep Learning Models for Streamflow Prediction

Accurately predicting streamflow using process-based models remains challenging due to uncertainties in model parameters and the complex nature of streamflow generation. Data-driven approaches, however, offer feasible alternatives, avoiding the need for physical process representation. This study introduces a hybrid deep learning framework, CNN-GRU-BiLSTM, for daily streamflow prediction. This model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and bidirectional long short-term memory (BiLSTM) networks to leverage their complementary strengths. When applied to the Neuse River Basin (NRB) (North Carolina, USA), the proposed model achieved strong predictive performance, yielding a root mean square (RMSE) of 11.8 m3/s (compared to an average streamflow of 132.7 m3/s), and a mean absolute error (MAE) of 8.7 m3/s, and a Nash–Sutcliffe efficiency (NSE) of 0.994 for the testing dataset. Similar performance trends were observed in the training and validation phases. A comparative analysis against seven other deep learning and hybrid models of similar complexity highlighted the outstanding performance of the CNN-GRU-BiLSTM model across all flood events. Furthermore, its stability, robustness, and transferability were evaluated in a seasonal dataset, peak floods, and different locations along the river. These findings underscore the potential of hybrid deep learning models and reinforce the effectiveness of integrating multiple data-driven techniques for streamflow prediction in regions where precipitation is the dominant driver of streamflow.