In coastal and open ocean human activities, there is an increasing demand for accurate estimates of future sea state. In these activities, predictions of wave heights and periods are of particular importance. In this study, two different neural network strategies were employed to forecast significant wave heights and zero-up-crossing wave periods 3, 6, 12 and 24 h in advance. In the first approach, eight simple separate neural nets were implemented to simulate every wave parameter over each prediction interval. In the second approach, only two networks provided simultaneous forecasts of these wave parameters for the four prediction intervals. Two independent sets of measurements from a directional wave buoy moored off the Portuguese west coast were used to train and to validate the artificial neural nets. Saliency analysis of the results permitted an optimization of the networks' architectures. The optimal learning algorithm for each case was also determined. The short-term forecasts of the wave parameters verified by actual observations demonstrate the suitability of the artificial neural
Many small wave-dominated inlets are naturally unstable and require regular dredging. To mitigate the costs of these operations, the dredged channels should be designed to bring the inlet close to equilibrium and minimise flood dominance. However, it is often unclear how to optimise the configuration of the channels. This study focuses on a small lagoon on the western Portuguese coast that has been subject to frequent and diverse interventions.A process-based morphodynamic model is applied to compare the dredging plan that has been followed for the last 15 years with two new alternatives. These alternative dredging plans increase the tidal prism and reduce flood
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