A framework for transformation to nearshore wave from global wave data using machine learning techniques: Validation at the Port of Hitachinaka, Japan
Sooyoul Kim, Tracey H. Tom, Masahide Takeda, Hajime MASE
Abstract
The present study introduces a framework for predicting nearshore waves using two machine learning techniques of Group Method of Data Handling (GMDH) and Artificial Neural Network (ANN), trained with three global wave datasets of Japan Meteorological Agency (JMA), National Oceanic and Atmospheric Administration (NOAA), and European Centre for Medium-Range Weather Forecasts (ECMWF). Prior to our ultimate goal of forecasting nearshore waves up to one week in advance, the current study challenges to hindcast nearshore wave heights and periods for a target year at the Port of Hitachinaka, Japan using the framework compounding GMDH and ANN trained with the initially forecasted (0 h) and reanalyzed two datasets. It was found that the GMDH-based wave model, trained with NOAA and ECMWF, well predicted observed significant wave heights, while a combination of JMA and ECMWF for training gave the best performance for significant wave periods. The same tendency was found when using ANN. Since the present framework successfully transforms global waves into local nearshore waves, it can be said that the framework for the nearshore wave prediction is able to support the one week ahead wave prediction and to be implemented at a particular location, where the nearshore wave observations are available.