Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
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 an...
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| Format: | article |
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Universitas Udayana
2021
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| Online Access: | https://doaj.org/article/2a9f2e5b0bf24b69b0db9e5f2c3d3a4d |
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| Summary: | 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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