Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait Article Swipe
Annas Wahyu Ramadhan
,
Didit Adytia
,
Deni Saepudin
,
Semeidi Husrin
,
Adiwijaya Adiwijaya
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24843/lkjiti.2021.v12.i03.p01
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24843/lkjiti.2021.v12.i03.p01
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24843/lkjiti.2021.v12.i03.p01
- https://ojs.unud.ac.id/index.php/lontar/article/download/75488/42119
- OA Status
- gold
- Cited By
- 8
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200066629
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- OpenAlex ID
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https://openalex.org/W4200066629Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24843/lkjiti.2021.v12.i03.p01Digital Object Identifier
- Title
-
Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda StraitWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-29Full publication date if available
- Authors
-
Annas Wahyu Ramadhan, Didit Adytia, Deni Saepudin, Semeidi Husrin, Adiwijaya AdiwijayaList of authors in order
- Landing page
-
https://doi.org/10.24843/lkjiti.2021.v12.i03.p01Publisher landing page
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https://ojs.unud.ac.id/index.php/lontar/article/download/75488/42119Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ojs.unud.ac.id/index.php/lontar/article/download/75488/42119Direct OA link when available
- Concepts
-
Recurrent neural network, Sea level, Time series, Series (stratigraphy), Climatology, Sea surface temperature, Artificial intelligence, Computer science, Artificial neural network, Meteorology, Machine learning, Oceanography, Geology, Geography, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
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21Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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