Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

  • Annas Wahyu Ramadhan School of Computing, Telkom University Bandung, Indonesia
  • Didit Adytia School of Computing, Telkom University.
  • Deni Saepudin School of Computing, Telkom University Bandung, Indonesia
  • Semeidi Husrin Marine Research Centre, Ministry of Marine Affairs and Fisheries of Indonesia Jakarta, Indonesia
  • Adiwijaya Adiwijaya School of Computing, Telkom University Bandung, Indonesia

Abstract

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cant
consequences in coastal operations, such as naval engineering and navigation. Conventional sea
level predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elements
and require long-term historical sea level data. In this paper, two deep learning approaches
are applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), and
the second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (Inexpensive
Device for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained the
model for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1st
May to 1st August. We compared forecasting results with RNN and LSTM with the results of the
conventional method, namely tidal harmonic analysis. The LSTM’s results showed better performance
than the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 and
an RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodate
non-tidal harmonic data such as sea level anomalies.

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Published
2021-10-29
How to Cite
RAMADHAN, Annas Wahyu et al. Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 3, p. 130-140, oct. 2021. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/75488>. Date accessed: 27 may 2022. doi: https://doi.org/10.24843/LKJITI.2021.v12.i03.p01.