Abstract
Floods are among the most common natural disasters, causing countless losses every year worldwide and demanding urgent measures to mitigate their impacts. This study proposes a novel combination of artificial intelligence and process-based models to construct a flood early warning system (FEWS) for estuarine regions. Using streamflow and rainfall data, a deep learning model with long short-term memory layers was used to forecast the river discharge at the fluvial boundary of an estuary. Afterwards, a hydrodynamic process-based model was used to simulate water levels in the estuary. The river discharge predictors were trained using different forecasting windows varying from 3 h to 36 h to assess the relationship between the time window and accuracy. The insertion of attention layers into the network architecture was evaluated to enhance forecasting capacity. The FEWS was implemented in the Douro River Estuary, a densely urbanised flood-prone area in northern Portugal. The results demonstrated that the Douro Estuary FEWS is reliable for discharges up to 5000 m 3 /s, with predictions made 36 h in advance. For values higher than this, the uncertainties in the model predictions increased; however, they were still capable of detecting flood occurrences.