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Improving sea level prediction in coastal areas using machine learning techniques

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dc.citedby2
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorAlmubaidin M.A.en_US
dc.contributor.authorShen C.G.en_US
dc.contributor.authorSapitang M.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57476845900en_US
dc.contributor.authorid59210032500en_US
dc.contributor.authorid57215211508en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:42:14Z
dc.date.available2025-03-03T07:42:14Z
dc.date.issued2024
dc.description.abstractThe objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data determined by the machine learning model's performance. Thirteen models were trained precisely and properly throughout the machine learning process. The results showed that SVM models provide good performance during the training process and attained relatively poor performance during testing process. On the other hand, the KNN model showed consistent performance for both training and testing process. Regarding the effectiveness of different kernels of the SVM algorithm, the Radial Basis Function (RBF) kernel is the most suitable, which provides the finest analysis for the sea level rise dataset and acceptable values for RSME, MAE, and R2. ? 2024 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102916
dc.identifier.doi10.1016/j.asej.2024.102916
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85197902654
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85197902654&doi=10.1016%2fj.asej.2024.102916&partnerID=40&md5=7e4135fd5e9fa1bac860ae4994459361
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36398
dc.identifier.volume15
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.subjectCoastal zones
dc.subjectLearning systems
dc.subjectMotion compensation
dc.subjectNearest neighbor search
dc.subjectRadial basis function networks
dc.subjectSea level
dc.subjectCoastal area
dc.subjectFlood modeling
dc.subjectK-near neighbor
dc.subjectK-nearest neighbour models
dc.subjectMachine learning techniques
dc.subjectMachine-learning
dc.subjectSupport vector machine
dc.subjectSupport vectors machine
dc.subjectTesting process
dc.subjectTraining process
dc.subjectSupport vector machines
dc.titleImproving sea level prediction in coastal areas using machine learning techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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