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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| Format: | Article |
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Taylor and Francis Ltd.
2021
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| 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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| Summary: | 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. |
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