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Time-series prediction of sea level change in the east coast of Peninsular Malaysia from the supervised learning approach

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dc.citedby9
dc.contributor.authorLai V.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAbdullah S.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid56509029800en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T08:09:36Z
dc.date.available2023-05-29T08:09:36Z
dc.date.issued2020
dc.descriptionDecision making; Forecasting; Learning algorithms; Support vector machines; Support vector regression; Surface waters; Tide gages; Correlation coefficient; Marine management; Meteorological parameters; Regression support vector machines; Sea surface temperature (SST); Supervised learning approaches; Tide gauge data; Time series prediction; Sea levelen_US
dc.description.abstractAnalyzing and predicting the rises in sea level are vital elements in oceanography and marine management especially in managing low-lying coastal areas. The present study aims to analyze the ability of machine learning algorithm viz. regression support vector machine (RSVM) in predicting the changes in the sea level on the east coast of Peninsular Malaysia. The selected inputs for the proposed model are monthly mean sea level (MMSL), monthly sea surface temperature (SST), rainfall and mean cloud cover (MCC) for the period from January 2007 to December 2017. A total of 132 data points for each meteorological parameter were used, where 92 (70%) data points from January 2007 to December 2015 were used for training and 40 (30%) data points from January 2016 to December 2017 were used for validating and testing. Results showed based on the correlation coefficient that the model predicts the sea level rises accurately (R= 0.861, 0.825 and 0.857) for Kerteh, Tanjung Sedili, and Tioman Island, respectively. Moreover, the predicted values were similar to the historical tide-gauge data with very low error, which indicates that the proposed RSVM model can be a promising tool for decision-makers and can be reliable to predict monthly mean sea level rises in Malaysia. � 2020 WITPress. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18280/ijdne.150314
dc.identifier.epage415
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85087835571
dc.identifier.spage409
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087835571&doi=10.18280%2fijdne.150314&partnerID=40&md5=ac5524177d180a9214fb459c723884ef
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25454
dc.identifier.volume15
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Design and Nature and Ecodynamics
dc.titleTime-series prediction of sea level change in the east coast of Peninsular Malaysia from the supervised learning approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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