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A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App

There is growing interest in the adoption of Engineering with Nature or Nature Based Solutions for coastal protection including large mega-nourishment interventions. However, there are still many unknowns on the variables and design features influencing their functionalities. There are also challeng...

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Autores principales: Kumar, Pavitra, Leonardi, Nicoletta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224936/
https://www.ncbi.nlm.nih.gov/pubmed/37244960
http://dx.doi.org/10.1038/s41598-023-35801-5
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author Kumar, Pavitra
Leonardi, Nicoletta
author_facet Kumar, Pavitra
Leonardi, Nicoletta
author_sort Kumar, Pavitra
collection PubMed
description There is growing interest in the adoption of Engineering with Nature or Nature Based Solutions for coastal protection including large mega-nourishment interventions. However, there are still many unknowns on the variables and design features influencing their functionalities. There are also challenges in the optimization of coastal modelling outputs or information usage in support of decision-making. In this study, more than five hundred numerical simulations with different sandengine designs and different locations along Morecambe Bay (UK) were conducted in Delft3D. Twelve Artificial Neural Networking ensemble models structures were trained on the simulated data to predict the influence of different sand engines on water depth, wave height and sediment transports with good performance. The ensemble models were then packed into a Sand Engine App developed in MATLAB and designed to calculate the impact of different sand engine features on the above variables based on users’ inputs of sandengine designs.
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spelling pubmed-102249362023-05-29 A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App Kumar, Pavitra Leonardi, Nicoletta Sci Rep Article There is growing interest in the adoption of Engineering with Nature or Nature Based Solutions for coastal protection including large mega-nourishment interventions. However, there are still many unknowns on the variables and design features influencing their functionalities. There are also challenges in the optimization of coastal modelling outputs or information usage in support of decision-making. In this study, more than five hundred numerical simulations with different sandengine designs and different locations along Morecambe Bay (UK) were conducted in Delft3D. Twelve Artificial Neural Networking ensemble models structures were trained on the simulated data to predict the influence of different sand engines on water depth, wave height and sediment transports with good performance. The ensemble models were then packed into a Sand Engine App developed in MATLAB and designed to calculate the impact of different sand engine features on the above variables based on users’ inputs of sandengine designs. Nature Publishing Group UK 2023-05-27 /pmc/articles/PMC10224936/ /pubmed/37244960 http://dx.doi.org/10.1038/s41598-023-35801-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kumar, Pavitra
Leonardi, Nicoletta
A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title_full A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title_fullStr A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title_full_unstemmed A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title_short A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App
title_sort novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the sand engine app
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224936/
https://www.ncbi.nlm.nih.gov/pubmed/37244960
http://dx.doi.org/10.1038/s41598-023-35801-5
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