Significant Wave Height Forecasting using Long-Short Term Memory (LSTM) in Seribu Island Waters

Abstract

Wind waves are natural phenomena primarily generated by the wind. Information about wave height and period is highly crucial in various marine fields such as coastal engineering, fisheries, and maritime transportation. However, accurately predicting wave height remains a challenge due to the stochastic nature of ocean waves themselves. Several approaches to predicting wave height have been developed, including numerical models and machine learning methods, such as the Long-Short Term Memory (LSTM) algorithm, which has currently garnered significant attention from researchers. The objective of this research is to develop a forecast model for wind wave height using the LSTM algorithm in Seibu Island Waters, DKI Jakarta. The ERA5 dataset comprises zonal and meridional wind components and significant wave height, along with wind measurement data using the Automatic Weather System (AWS) instrument, are used to train and test to train and test the LSTM model. The research results show that the LSTM model can predict significant wave height effectively. Predictions using the ERA5 significant height dataset are observed to be closer to field data, with RMSE, MAE, and MAPE values of 0.1535 m, 0.1181 m, and 37.11% respectively. Thus, the model evaluation results indicate good performance, with relatively low RMSE and MAE values, and a good MAPE value. The highest accuracy in significant wave height prediction is found for forecasts one week (7 days) ahead

Keywords

• deep learning, forecast, lstm, ocean wave, significant wave height