Author:
Kumar Pavitra,Leonardi Nicoletta
Abstract
AbstractThere 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.
Funder
UK Research and Innovation
Publisher
Springer Science and Business Media LLC
Reference77 articles.
1. Fox-Kemper, B. et al. Ocean, cryosphere and sea level change, in in climate change 2021: The physical science basis. In Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1211–1362 (Cambridge University Press, 2021).
2. Vousdoukas, M. I. et al. Sandy coastlines under threat of erosion. Nat. Clim. Chang. 10(3), 260–263 (2020).
3. Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl. Acad. Sci. 111(9), 3292–3297 (2014).
4. Herman, P. M. J. et al. A mega-nourishment (sand motor) affects landscape diversity of subtidal benthic fauna. Front. Mar. Sci. 8, 643674 (2021).
5. Luijendijk, A. et al. The state of the world’s beaches. Sci. Rep. 8(1), 6641 (2018).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献