Kumar, Pavitra 
ORCID: 0000-0002-4683-724X and Leonardi, Nicoletta
(2023)
Coastal forecast through coupling of Artificial Intelligence and hydro-morphodynamical modelling.
COASTAL ENGINEERING JOURNAL, 65 (3).
pp. 450-469.
Abstract
As climate-driven risks for the world’s coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change. Artificial Intelligence is a powerful technology that has been rapidly evolving recently and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of Artificial Intelligence models were tested, one set relying on localized modeling outputs or localized data sources and another set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing, respectively. The second set of reduced dependency models provides regression values greater than 0.84 and 0.76 for training and testing, respectively. Our results highlight the potential of AI and statistical models for coastal applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Morphological changes, Sediment transport, Neural networks, Bayesian networks, Delft3d, > |
| Divisions: | Faculty of Science and Engineering > School of Environmental Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Jul 2023 13:49 |
| Last Modified: | 16 Sep 2023 13:07 |
| DOI: | 10.1080/21664250.2023.2233724 |
| Open Access URL: | https://doi.org/10.1080/21664250.2023.2233724 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3171971 |
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