Artificial Intelligence for estimating infragravity energy in a harbour

The estimation of long waves inside a harbour is a matter of great importance for port management. The objective of this work is to apply Artificial Intelligence to estimate the significant infragravity wave height inside a harbour. Two Artificial Neural Network (ANN) models with the same input (the short wave parameters outside the harbour and the tidal level) are developed and compared. The first is a one-step model that estimates the significant infragravity wave height inside the harbour directly. The second is a two-step model that computes the infragravity wave height first outside, then inside the harbour. The two models are trained and successfully validated based on observations at the Port of Ferrol (NW Spain), where seiching is known to occur. The network architecture that performs best for each model is selected using a k-fold cross-validation method. The estimation of the infragravity wave height outside the harbour with the two-step model is shown to be more accurate than that from a widely used empirical expression. As regards the all-important estimation inside the harbour, the one-step model is found to perform better than its two-step counterpart.

► We apply ANNs for predicting infragravity wave energy inside and outside a harbour.
► We use a k-fold cross-validation method to select the best ANN architecture.
► A direct (one-step) model performs better than an indirect (two-step) model.
► The ANN model outperforms a well-known empirical expression.