Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia
MetadataShow full item record
O. Makarynskyy, , D. Makarynska, M. Kuhn and W.E. Featherstone(2004) Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science 61(2):351-360.
The link to this article is:
Copyright 2004 Elsevier B.V. All rights reserved
In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 hours, 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).
Showing items related by title, author, creator and subject.
Chow, Chi Ngok (2010)The largest wool exporter in the world is Australia, where wool being a major export is worth over AUD $2 billion per year and constitutes about 17 per cent of all agricultural exports. Most Australian wool is sold by ...
Mostafa, Fahed. (2011)Market risk refers to the potential loss that can be incurred as a result of movements inmarket factors. Capturing and measuring these factors are crucial in understanding andevaluating the risk exposure associated with ...
Badrzadeh, Honey; Sarukkalige, Priyantha; Jayawardena, A. (2012)Achieving accurate intermittent river flow forecasting, plays a key role in water resources and environmental management. Water demands are increasing while surface water availability is likely to decrease in Western ...