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Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models

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Resumo: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 m3 /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.
Autores principais:Melo, Willian Weber
Outros Autores:Iglesias, Isabel; Pinho, José L. S.
Assunto:Flood early warning system Deep learning Numerical modelling Long short-term memory neural networks TensorFlow Douro river Disaster risk reduction Engenharia e Tecnologia::Engenharia Civil Ação climática
Ano:2025
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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