A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future

In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia�s coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level...

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Main Authors: Adebisi, N., Balogun, A.-L.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29476 /
Published: Taylor and Francis Ltd. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111847855&doi=10.1080%2f10106049.2021.1958015&partnerID=40&md5=04b087ef0ff1b499e0d34e5f1db40c4a
http://eprints.utp.edu.my/29476/
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id utp-eprints.29476
recordtype eprints
spelling utp-eprints.294762022-03-25T02:07:29Z A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future Adebisi, N. Balogun, A.-L. In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia�s coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. © 2021 Informa UK Limited, trading as Taylor & Francis Group. Taylor and Francis Ltd. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111847855&doi=10.1080%2f10106049.2021.1958015&partnerID=40&md5=04b087ef0ff1b499e0d34e5f1db40c4a Adebisi, N. and Balogun, A.-L. (2021) A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future. Geocarto International . http://eprints.utp.edu.my/29476/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia�s coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
format Article
author Adebisi, N.
Balogun, A.-L.
spellingShingle Adebisi, N.
Balogun, A.-L.
A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
author_sort Adebisi, N.
title A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
title_short A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
title_full A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
title_fullStr A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
title_full_unstemmed A deep-learning model for national scale modelling and mapping of sea level rise in Malaysia: the past, present, and future
title_sort deep-learning model for national scale modelling and mapping of sea level rise in malaysia: the past, present, and future
publisher Taylor and Francis Ltd.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111847855&doi=10.1080%2f10106049.2021.1958015&partnerID=40&md5=04b087ef0ff1b499e0d34e5f1db40c4a
http://eprints.utp.edu.my/29476/
_version_ 1741197247822954496
score 11.62408

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