Transfer learning for streamflow forecasting using unguaged MOPEX basins data set

Floods are significant global hazards that generally lead to loss of lives and billions worth of properties, especially in flood-prone regions. Various excellent artificial intelligence algorithms that are aimed at predicting streamflow to minimize the effects of floods are proposed by researchers. Most of these models depend on the assumption that both training and testing data set are similar and sufficient. However, in reality, many of these data set varies along with time and are insufficient in some new basins. Motivated by the success of transfer learning in natural language processing, image processing and time series forecasting. In this paper, we proposed two hybrid transfer learning models for streamflow forecasting. The proposed models, which integrate Gated Recurrent Unit (GRU) with transfer learning and integration of Long Short Term Memory (LSTM) with transfer learning, are compared to our reference model. The proposed coupled Transfer Learning with the GRU (TL+GRU) model outperforms the baseline models, i.e., the transfer learning model and the coupled Transfer Learning with LSTM model (TL+LSTM) for most of the basins when streamflow and precipitation data set from Model Parameter Estimation Experiment (MOPEX) basins in the United States of America is used. As a result, we can finally conclude that, with Artificial Neural Networks’ (ANN) integration to transfer learning, more enhanced performance are obtained.