DEVELOPMENT OF 2D-WAVE FIELD PREDICTION MODEL THAT TAKES INTO ACCOUNT LAND AND MULTI-TIME METEOROLOGICAL FIELDS

Abstract

In recent years, neural networks and deep learning have been studied in the field of coastal engineering. In particular, deep learning has been widely used in wave prediction because of its superiority in computational cost over the numerical simulations that have been used in the past. However, deep learning in previous studies has mostly focused on pinpoint prediction at a single point, and little research has been conducted on the prediction of areal wave fields. In this study, we propose a method to obtain the spatial distribution of waves by deep learning using a weather field forecasted by numerical simulation as input. The proposed method improves the accuracy by introducing a weighting to the loss function to eliminate the influence of the boundary conditions on the learning over land. In addition, the accuracy of wave prediction was improved by learning meteorological fields at multiple times as input data.