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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Autores principales:	Annas Wahyu Ramadhan, Didit Adytia, Deni Saepudin, Semeidi Husrin, Adiwijaya Adiwijaya
Formato:	article
Lenguaje:	ID
Publicado:	Universitas Udayana 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2a9f2e5b0bf24b69b0db9e5f2c3d3a4d
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spelling	oai:doaj.org-article:2a9f2e5b0bf24b69b0db9e5f2c3d3a4d2021-12-01T04:18:07ZForecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait2088-15412541-583210.24843/LKJITI.2021.v12.i03.p01https://doaj.org/article/2a9f2e5b0bf24b69b0db9e5f2c3d3a4d2021-10-01T00:00:00Zhttps://ojs.unud.ac.id/index.php/lontar/article/view/75488https://doaj.org/toc/2088-1541https://doaj.org/toc/2541-5832Sea-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.Annas Wahyu RamadhanDidit AdytiaDeni SaepudinSemeidi HusrinAdiwijaya AdiwijayaUniversitas UdayanaarticleElectronic computers. Computer scienceQA75.5-76.95IDLontar Komputer, Vol 12, Iss 3, Pp 130-140 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Annas Wahyu Ramadhan Didit Adytia Deni Saepudin Semeidi Husrin Adiwijaya Adiwijaya Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
description	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.
format	article
author	Annas Wahyu Ramadhan Didit Adytia Deni Saepudin Semeidi Husrin Adiwijaya Adiwijaya
author_facet	Annas Wahyu Ramadhan Didit Adytia Deni Saepudin Semeidi Husrin Adiwijaya Adiwijaya
author_sort	Annas Wahyu Ramadhan
title	Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
title_short	Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
title_full	Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
title_fullStr	Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
title_full_unstemmed	Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait
title_sort	forecasting of sea level time series using rnn and lstm case study in sunda strait
publisher	Universitas Udayana
publishDate	2021
url	https://doaj.org/article/2a9f2e5b0bf24b69b0db9e5f2c3d3a4d
work_keys_str_mv	AT annaswahyuramadhan forecastingofsealeveltimeseriesusingrnnandlstmcasestudyinsundastrait AT diditadytia forecastingofsealeveltimeseriesusingrnnandlstmcasestudyinsundastrait AT denisaepudin forecastingofsealeveltimeseriesusingrnnandlstmcasestudyinsundastrait AT semeidihusrin forecastingofsealeveltimeseriesusingrnnandlstmcasestudyinsundastrait AT adiwijayaadiwijaya forecastingofsealeveltimeseriesusingrnnandlstmcasestudyinsundastrait
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Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

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