Enhancing streamflow predictions through basin-to-basin knowledge transfer: A novel strategy for deep learning models adaptation and generalization

Accurate prediction of daily streamflow is critical for effective water resource management. In this paper, we present a novel strategy for deep learning generalization and adaptation to improve daily streamflow prediction in a mountainous region. The process is realized using Bayesian optimization; a Cross Basin Transfer approach to select the stable model; finally, improving predictions using knowledge transfer. The Gated Recurrent Unit model was found to be stable in a complex and diverse sub-basin, while, the Long Short-Term Memory model is robust in a more homogeneous environment. In contrast, the One-Dimensional Convolutional Neural Network had poor accuracy and was unable to capture the flow dynamics. Moreover, the transferred model showed improvement, with the efficiency increasing from 0.20 to 0.51. Our strategy of adapting deep learning models based on the generalization of different areas may represent a valuable approach in the application related to water resources management.