Coastal morphodynamic emulator for early warning short-term forecasts

Melo, Willian; Pinho, José L. S.; Iglésias, I.

doi:10.1016/j.envsoft.2023.105729

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…While the MLP model had the best accuracy among the four models, an ensemble method of averaged weights of the four models provided the best skill across all storms studied and the most accurate performance above any separate model. On the other hand, Melo et al [99] attempted to solve a full 2D morphological domain, not just transects, by replicating the morphological evolution of complex coasts involving a groin and a breakwater by using a hybrid technique. To reduce the computational cost of Xbeach to compute morphological evolution, the authors developed an emulator of the morphological module capable of reproducing images of current and cumulative bed level changes.…”

Section: Morphology Changementioning

confidence: 99%

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Abouhalima,

das Neves,

Taveira-Pinto

et al. 2024

JMSE

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The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

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Section: Morphology Changementioning

confidence: 99%

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Abouhalima,

das Neves,

Taveira-Pinto

et al. 2024

JMSE

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A Systematic Literature Review on Regression Machine Learning for Urban Flood Hazard Mapping

El Baida,

Boushaba,

Chourak

et al. 2024

Lecture Notes in Networks and Systems

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

Weber de Melo,

Iglesias,

Pinho

2024

Nat Hazards

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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.

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Coastal morphodynamic emulator for early warning short-term forecasts

Cited by 5 publications

References 23 publications

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

A Systematic Literature Review on Regression Machine Learning for Urban Flood Hazard Mapping

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

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