Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia

In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 h, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the me...

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Bibliographic Details
Published in:Estuarine, coastal and shelf science coastal and shelf science, 2004-10, Vol.61 (2), p.351-360
Main Authors: Makarynskyy, O., Makarynska, D., Kuhn, M., Featherstone, W.E.
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
artificial neural networks
Biological and medical sciences
Brackish water ecosystems
forecast
Fundamental and applied biological sciences. Psychology
sea level variations
Synecology
tide gauge
Western Australian coast
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Summary:In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 h, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, for the period December 1991–December 2002 were used to train and to validate the employed neural networks. The results obtained show the feasibility of the neural sea level forecasts in terms of the correlation coefficient (0.7–0.9), root mean square error (about 10% of tidal range) and scatter index (0.1–0.2).
ISSN:0272-7714
1096-0015
DOI:10.1016/j.ecss.2004.06.004