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

East 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 implicati...

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Main Authors: Lai V., Ahmed A.N., Malek M.A., El-Shafie A.
Other Authors: 57204919704
Format: Article
Published: IAEME Publication 2023
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id my.uniten.dspace-23621
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spelling my.uniten.dspace-236212023-05-29T14:50:33Z Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach Lai V. Ahmed A.N. Malek M.A. El-Shafie A. El-Shafie A. 57204919704 57214837520 55636320055 16068189400 57207789882 East 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. Final 2023-05-29T06:50:33Z 2023-05-29T06:50:33Z 2018 Article 2-s2.0-85057894675 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057894675&partnerID=40&md5=6dc4e7940fc742c617c8a219c7250fe6 https://irepository.uniten.edu.my/handle/123456789/23621 9 11 1404 1413 IAEME Publication Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description East 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.
author2 57204919704
author_facet 57204919704
Lai V.
Ahmed A.N.
Malek M.A.
El-Shafie A.
El-Shafie A.
format Article
author Lai V.
Ahmed A.N.
Malek M.A.
El-Shafie A.
El-Shafie A.
spellingShingle Lai V.
Ahmed A.N.
Malek M.A.
El-Shafie A.
El-Shafie A.
Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
author_sort Lai V.
title Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
title_short Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
title_full Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
title_fullStr Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
title_full_unstemmed Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
title_sort evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach
publisher IAEME Publication
publishDate 2023
_version_ 1806426331090518016
score 13.222552

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