Ocean current speed prediction model in the sunda strait using Long Short-Term Memory (LSTM)

Anton Daud , Khomsin , Danar Guruh Pratomo , Agie Wandala Putera

Abstract

This research focuses on predicting the speed of ocean currents in the Sunda Strait by employing a Long Short-Term Memory (LSTM) model based on historical data. The approach includes data preprocessing, normalization of features using MinMaxScaler, segmentation of the data into training and testing sets, and the development of layered LSTM model architecture. The dataset comprises longitude, latitude, current velocity, and time information from 2022 to 2024. The findings indicate that the LSTM model can predict ocean current speeds with a Root Mean Squared Error (RMSE) of 13.66 cm/s, a mean absolute error (MAE) of 9.06 cm/s, and a determination coefficient (R) of 0.87. The demonstration illustrated the typical design of ocean current speed fluctuations; however, forecasting unusual variations remains challenging. In summary, the LSTM model represents a practical approach for predicting ocean currents based on historical data, aiming to enhance prediction accuracy. This model will support navigation efforts and marine resource management in the Sunda Strait region.

Key Words

historical data; LSTM; ocean currents; prediction; Sunda Strait

Address

Anton Daud — Department of Geomatic Engineering, Institute Technology Sepuluh Nopember, Surabaya, Indonesia; Meteorology, Climatology, and Geophysics Agency, Jakarta, Indonesia
Khomsin, Danar Guruh Pratomo — Department of Geomatic Engineering, Institute Technology Sepuluh Nopember, Surabaya, Indonesia
Agie Wandala Putera — Meteorology, Climatology, and Geophysics Agency, Jakarta, Indonesia

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