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 1 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 (80–100 mm), and scatter index (0.1–0.2) when compared with actual observations. Therefore, the proposed methodology could be successfully used for site-specific forecasts.