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Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

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dc.citedby18
dc.contributor.authorLai V.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorIbrahim R.K.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57188832586en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid57207789882en_US
dc.date.accessioned2023-05-29T07:23:53Z
dc.date.available2023-05-29T07:23:53Z
dc.date.issued2019
dc.descriptioncoastal zone; design method; genetic algorithm; machine learning; prediction; sea level change; support vector machine; Malaysia; Pahang; Seribuat Archipelago; Tioman; West Malaysiaen_US
dc.description.abstractThe estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4643
dc.identifier.doi10.3390/su11174643
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85071976838
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071976838&doi=10.3390%2fsu11174643&partnerID=40&md5=ec0e006437ed7ce67e580dd5ce88a105
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24481
dc.identifier.volume11
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleModeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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