Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/86781
Título: | Emulating the estuarine morphology evolution using a deep convolutional neural network emulator based on hydrodynamic results of a numerical model |
Autor(es): | Weber de Melo, Willian Pinho, José L. S. Iglesias, Isabel |
Palavras-chave: | artificial intelligence convolutional neural networks Delft3D hydro-morphodynamics numerical model emulator TensorFlow |
Data: | 1-Nov-2022 |
Editora: | IWA Publishing |
Revista: | Journal of Hydroinformatics |
Citação: | Weber de Melo, W., Pinho, J. L. S., & Iglesias, I. (2022, September 20). Emulating the estuarine morphology evolution using a deep convolutional neural network emulator based on hydrodynamic results of a numerical model. Journal of Hydroinformatics. IWA Publishing. http://doi.org/10.2166/hydro.2022.068 |
Resumo(s): | Coastal and estuarine areas present remarkable environmental values, being key zones for the development of many human activities such as tourism, industry, fishing, and other ecosystem services. To promote the sustainable use of these services, effectively managing these areas and their water and sediment resources for present and future conditions is of utmost importance to implement operational forecast platforms using real-time data and numerical models. These platforms are commonly based on numerical modelling suites, which can simulate hydro-morphodynamic patterns with considerable accuracy. However, in many cases, considering the high spatial resolution models that are necessary to develop operational forecast platforms, a high computing capacity is also required, namely for data processing and storage. This work proposes the use of artificial intelligence (AI) models to emulate morphodynamic numerical model results, allowing us to optimize the use of computational resources. A convolutional neural network was implemented, demonstrating its capacity in reproducing the erosion and sedimentation patterns, resembling the numerical model results. The obtained root mean squared error was 0.59 cm, and 74.5 years of morphological evolution was emulated in less than 5 s. The viability of surrogating numerical models by AI techniques to forecast the morphological evolution of estuarine regions was clearly demonstrated. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/86781 |
DOI: | 10.2166/hydro.2022.068 |
ISSN: | 1464-7141 |
e-ISSN: | 1465-1734 |
Versão da editora: | https://iwaponline.com/jh/article/24/6/1254/91074/Emulating-the-estuarine-morphology-evolution-using |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | BUM - CIÊNCIAVITAE |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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jh0241254.pdf | 694 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons