Articles | Volume 12, issue 2
https://doi.org/10.5194/ascmo-12-221-2026
https://doi.org/10.5194/ascmo-12-221-2026
15 Sep 2026
 | 15 Sep 2026

Comparative evaluation of statistical and deep learning methods for high-frequency radar surface current forecasting in a narrow tropical strait

Dhava Gautama and Alifficionaldo Agpri Putra

Model code and software

Analysis code for: Comparative evaluation of statistical and deep learning methods for high-frequency radar surface current forecasting in a narrow tropical strait Dhava Gautama and Alifficionaldo A. Putra https://doi.org/10.5281/zenodo.20580835

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Short summary
This study compares different methods to predict ocean surface currents in a fast-changing strait. Using over three years of radar observations, we tested both traditional and modern data-driven approaches. The results show that advanced learning models give the most accurate forecasts, especially for several hours ahead. We also find that including enough past information improves predictions. These findings can help improve maritime safety and support better decision-making in coastal areas.
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