Estimation of global coastal sea level extremes using neural networks
Bibliographic Data
| ID | 15548400 |
|---|---|
| Authors | Nicolas Bruneau (0000-0002-9017-1000, National Oceanography Centre, corresponding author), Jeff A Polton (0000-0003-0131-5250, National Oceanography Centre), Joanne Williams (0000-0002-5633-1592, National Oceanography Centre), Jason Holt (0000-0002-3298-8477, National Oceanography Centre) |
| Year | 2020 |
| Volume | 15 |
| Issue | 7 |
| Pages | 074030-074030 |
| Publication date | 2020-04-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ab89d6 |
| OpenAlex | W3017371428 |
| Language | EN |
| Citations received | 1 |
| References cited | 31 |
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
Artificial neural network · Climatology · Geography · Machine learning · Meteorology · Probabilistic logic · Resilience (materials science · Sea level · Storm · Storm surge · Tide gauge · Computer Science · Environmental Science · Hydrological Forecasting Using AI · Oceanographic and Atmospheric Processes · Tropical and Extratropical Cyclones Research · Artificial Intelligence · Geology · Oceanography
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |