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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia

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dc.citedby5
dc.contributor.authorHazrin N.A.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorNg J.L.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorTan K.W.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-shafie A.en_US
dc.contributor.authorid58550394200en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid54786091800en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:45Z
dc.date.available2024-10-14T03:17:45Z
dc.date.issued2023
dc.description.abstractIn consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. � 2023en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe19426
dc.identifier.doi10.1016/j.heliyon.2023.e19426
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85168853997
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85168853997&doi=10.1016%2fj.heliyon.2023.e19426&partnerID=40&md5=8125708b70c895e264e6c132019e0772
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34045
dc.identifier.volume9
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleHeliyon
dc.subjectCoastal regions
dc.subjectMachine learning
dc.subjectSea level rise prediction
dc.titlePredicting sea levels using ML algorithms in selected locations along coastal Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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