Predicting sea-level variations at the Cocos (Keeling) Islands with artificial neural networks

Sea-level variations affect the construction and management of coastal structures, near-shore navigation, coastal rivers' hydrological regime, and coastal tourism. Estimates of sea-level with hours-to-days warning times are especially important for low-lying regions, such as the Cocos (Keeling) Islands in the Indian Ocean. This study employs the technique of artificial neural networks to predict sea-level variations with warning times from I h to 5 days on the basis of hourly tide gauge observations. The data from the Cocos (Keeling) Islands SEAFRAME tide station for the period from 1992 to 2003 were used here. Feed-forward three-layered artificial neural networks were implemented to simulate sea level. The proposed neural methodology demonstrated reliable results in terms of the correlation coefficient (0.85-0.95). root mean square error (80-100mm), and scatter index (0.1-0.2) when compared with actual observations. Therefore, the proposed methodology could be successfully used for site-specific forecasts. (c) 2008 Elsevier Ltd. All rights reserved.