Publication:
Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach

Simple item page
dc.citedby4
dc.contributor.authorLai V.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.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.authorid16068189400en_US
dc.contributor.authorid57207789882en_US
dc.date.accessioned2023-05-29T06:50:33Z
dc.date.available2023-05-29T06:50:33Z
dc.date.issued2018
dc.description.abstractEast coast peninsular Malaysia (ECPM) has a sandy shoreline, and is dominated by low-lying regions that are exposed to severe storms, particularly during the Northeast Monsoon, making them vulnerable to erosion. This paper seeks to predict the sea level in ECPM. This study has an important implication for the population in ECPM since the predicted sea level could be used as an early warning signal to help prevent severe erosion and facilitate early evacuation of affected communities in case of flood inundation. Genetic Programming (GP) algorithm is an example of an evolutionary algorithm (EA) in the field of evolutionally computation (EC) and, more broadly, in Artificial Intelligence. GP is a meta-heuristic search and optimization technique based on natural evolution. The control and optimization parameters in this study are tuned. The findings obtained using the proposed model indicate that GP is able to make a good prediction of monthly mean sea level (MMSL) for a horizon of 10 years ahead for Kerteh, with a testing stage correlation coefficient (C.C) of 0.810 and the 300generation runs. A separate analysis was done for two other regions, Tioman Island and TanjungSedili, to compare the strength and consistency of the model. � 2018 IAEME Publication.en_US
dc.description.natureFinalen_US
dc.identifier.epage1413
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85057894675
dc.identifier.spage1404
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057894675&partnerID=40&md5=6dc4e7940fc742c617c8a219c7250fe6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23621
dc.identifier.volume9
dc.publisherIAEME Publicationen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Civil Engineering and Technology
dc.titleEvolutionary algorithm for forecastng mean sea level based on meta-heuristic approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS