Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration

Recently, it has been demonstrated that the use of lagged discharge in long short-term memory (LSTM) networks represents an effective method for streamflow prediction, so-called, data integration (DI). However, it is uncertain if a transfer learning (TL) model, which did not include recent discharge when trained on the source region, can reap the benefits of including recent discharge data. This study investigated the ability of TL to provide daily streamflow predictions for a few watersheds in a snow-dominated region (target) while transferring the knowledge acquired from the conterminous United States (source) based on an LSTM architecture. The performance of the TL model was compared with that of a physically based model (PBM), HYDROTEL. Additionally, testing the source model on the target region demonstrated the performance of the model used in TL. The approaches applied to improve the accuracy of TL included use of: (i) DI of recent observed flows and simulated flows (HYDROTEL) to improve predictions, (ii) different meteorological and physiographic variables from the source and target datasets, (iii) incremental numbers of training watersheds from the target region. Testing the source model with DI at the target region produced test-period median Kling-Gupta-efficiency (KGE) and Nash-Sutcliffe-log-model-efficiency (Nash-log) values of 0.837 and 0.870, respectively. The best performance was achieved with the TL model using DI with a maximum number of watersheds (median values of 0.953 and 0.942 for Nash-log and KGE, respectively). Moreover, including PBM simulated flows improved predictions, reducing the variability of performance metrics. Our findings show that TL and additional new procedures could significantly enhance streamflow predictions even when DI models are available.