Journal
ENVIRONMENTAL RESEARCH LETTERS
Volume 15, Issue 7, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1748-9326/ab89d6
Keywords
sea water anomaly; extremes; storm surges; GESLA database; machine learning
Funding
- 'Rapid Tidal Flow Forecasting for Marine Energy Resource Assessment', National Environmental Research Council (NERC) Innovation Pathfinder award [NE/S005811/1]
- NERC under National Capability Official Development Assistance (NC-ODA), ACCORD programme
- NERC [NE/S005811/1, NE/R000123/1, noc010010] Funding Source: UKRI
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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.
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