Estimation of global coastal sea level extremes using neural networks

Bruneau, Nicolas; Polton, Jeffrey ORCID: https://orcid.org/0000-0003-0131-5250; Williams, Joanne ORCID: https://orcid.org/0000-0002-8421-4481; Holt, Jason ORCID: https://orcid.org/0000-0002-3298-8477. 2020 Estimation of global coastal sea level extremes using neural networks. Environmental Research Letters, 15 (7), 074030. https://doi.org/10.1088/1748-9326/ab89d6

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Official URL: http://dx.doi.org/10.1088/1748-9326/ab89d6

Abstract/Summary

Accurately predicting total sea-level including tides and storm surges is key to protecting and managing our coastal environment. However, dynamically forecasting sea level extremes is computationally expensive. Here a novel alternative based on ensembles of artificial neural networks independently trained at over 600 tide gauges around the world, is used to predict the total sea-level based on tidal harmonics and atmospheric conditions at each site. The results show globally-consistent high skill of the neural networks (NNs) to capture the sea variability at gauges around the globe. While the main atmosphere-driven dynamics can be captured with multivariate linear regressions, atmospheric-driven intensification, tide-surge and tide-tide non-linearities in complex coastal environments are only predicted with the NNs. In addition, the non-linear NN approach provides a simple and consistent framework to assess the uncertainty through a probabilistic forecast. These new and cheap methods are relatively easy to setup and could be a valuable tool combined with more expensive dynamical model in order to improve local resilience.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1088/1748-9326/ab89d6
ISSN:	1748-9326
Date made live:	20 May 2020 14:47 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/527794

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1088/1748-9326/ab89d6

ISSN:

1748-9326

Date made live:

20 May 2020 14:47 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/527794