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)

Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Vol 12 No 3 (2021): Vol. 12, No. 03 December 2021

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