Wave Simulation Based Recurrent Neural Network Long-Short Term Memory (RNN-LSTM) - Wimala Lalitya Dhanistha; Fajar Sebastian; Haryo Dwito Armono (International Journal of Offshore and Coastal Engineering, 2026)
The Recurrent Neural Network Long-Short Term Memory (RNN-LSTM) algorithm was used to perform 6-hour time series forecasting for the variables significant wave height (Hs), maximum wave height (Hmax), zero-crossing period (Tz), and peak period (Tp) in a sea wave simulation using five datasets, each with a duration of ten years and from various locations. The ability of the RNN-LSTM algorithm to simulate sequential data, including time series, led to its selection. According to the research findings, the five RNN LSTM simulations produced fairly good forecasts for Tz (MAPE below 11%) and good forecasts for Hs and Hmax (MAPE below 9%). However, they had trouble simulating Tp (MAPE up to 34.06%). The tendency for Tp data to have a higher standard deviation (up to 2) than other variables and Tp's lower correlation with the other variables are factors that make Tp forecasting challenging. We find that RNN-LSTM can forecast Tp with moderate accuracy, but it can produce dependable forecasts for Hs, Hmax, and Tz