Publication:
Predicting Sea Level Rise Using Artificial Intelligence: A Review

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dc.citedby12
dc.contributor.authorBahari N.A.A.B.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorLai V.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorNg J.L.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57199323205en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:57Z
dc.date.available2024-10-14T03:17:57Z
dc.date.issued2023
dc.description.abstractForecasting sea level is critical for coastal structure building and port operations. There are, however, challenges in making these predictions, resulting from the complicated processes at various periods. This study discussed the continual development of the application and forecasting approaches for sea level rise, in standard and advanced modeling versions. To date, the tide gauge and satellite altimetry are the commonly used approaches for sea level measurement. Tide gauges are mostly deficient in typical offshore circumstancesen_US
dc.description.abstractbut however, this may be compensated for with satellite altimetry, a complementing technique. With technological improvement, sea level measurement may be forecasted using a variety of computer science approaches known as artificial intelligence, including machine learning and deep learningen_US
dc.description.abstractcapable of extracting information and formulating relationships from the given dataset. Its potential and extensive advantages led to a sharp growth in its recognition among hydrologists. The most successful techniques for enhancing these approaches include hybridization, ensemble modeling, data decomposition, and algorithm optimization. These advanced techniques are a prominent study area and a viable strategy for determining intelligent forecasts of sea level rise with sufficient lead time. For improved performance, the modeling requires incorporating numerous input parameters, such as precipitation, wind direction, ocean current, and sea surface temperatureen_US
dc.description.abstractfor better representing the process, thus reducing forecast error and uncertainty. Deep learning is more effective and enhances existing machine learning models for forecasting future sea level rise due to its automatic feature extraction and memory-storing capability. � 2023, The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE).en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11831-023-09934-9
dc.identifier.epage4062
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85153963902
dc.identifier.spage4045
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85153963902&doi=10.1007%2fs11831-023-09934-9&partnerID=40&md5=92a4d0389cc71d90c63d410f25f87967
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34097
dc.identifier.volume30
dc.pagecount17
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleArchives of Computational Methods in Engineering
dc.subjectDeep learning
dc.subjectForecasting
dc.subjectLearning systems
dc.subjectLevel measurement
dc.subjectNumerical methods
dc.subjectOffshore oil well production
dc.subjectSurface waters
dc.subjectTide gages
dc.subjectUncertainty analysis
dc.subjectAdvanced modeling
dc.subjectBuilding operations
dc.subjectCoastal structures
dc.subjectOffshores
dc.subjectPort operations
dc.subjectSatellite altimetry
dc.subjectSea level rise
dc.subjectStandard model
dc.subjectTechnological improvements
dc.subjectTide gauges
dc.subjectSea level
dc.titlePredicting Sea Level Rise Using Artificial Intelligence: A Reviewen_US
dc.typeReviewen_US
dspace.entity.typePublication
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