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Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

coastal zone; design method; genetic algorithm; machine learning; prediction; sea level change; support vector machine; Malaysia; Pahang; Seribuat Archipelago; Tioman; West Malaysia

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Bibliographic Details
Main Authors: Lai V., Ahmed A.N., Malek M.A., Afan H.A., Ibrahim R.K., El-Shafie A.
Other Authors: 57204919704
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-244812023-05-29T15:23:53Z Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms Lai V. Ahmed A.N. Malek M.A. Afan H.A. Ibrahim R.K. El-Shafie A. El-Shafie A. 57204919704 57214837520 55636320055 56436626600 57188832586 16068189400 57207789882 coastal zone; design method; genetic algorithm; machine learning; prediction; sea level change; support vector machine; Malaysia; Pahang; Seribuat Archipelago; Tioman; West Malaysia The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis. � 2019 by the authors. Final 2023-05-29T07:23:53Z 2023-05-29T07:23:53Z 2019 Article 10.3390/su11174643 2-s2.0-85071976838 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071976838&doi=10.3390%2fsu11174643&partnerID=40&md5=ec0e006437ed7ce67e580dd5ce88a105 https://irepository.uniten.edu.my/handle/123456789/24481 11 17 4643 All Open Access, Gold, Green MDPI 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 coastal zone; design method; genetic algorithm; machine learning; prediction; sea level change; support vector machine; Malaysia; Pahang; Seribuat Archipelago; Tioman; West Malaysia
author2 57204919704
author_facet 57204919704
Lai V.
Ahmed A.N.
Malek M.A.
Afan H.A.
Ibrahim R.K.
El-Shafie A.
El-Shafie A.
format Article
author Lai V.
Ahmed A.N.
Malek M.A.
Afan H.A.
Ibrahim R.K.
El-Shafie A.
El-Shafie A.
spellingShingle Lai V.
Ahmed A.N.
Malek M.A.
Afan H.A.
Ibrahim R.K.
El-Shafie A.
El-Shafie A.
Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
author_sort Lai V.
title Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
title_short Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
title_full Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
title_fullStr Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
title_full_unstemmed Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms
title_sort modeling the nonlinearity of sea level oscillations in the malaysian coastal areas using machine learning algorithms
publisher MDPI
publishDate 2023
_version_ 1806427491998367744
score 13.222552

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