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

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@Article{lai:2019:Sustainability,

• author = "Vivien Lai and Ali Najah Ahmed and M. A. Malek and Haitham {Abdulmohsin Afan} and Rusul Khaleel Ibrahim and Ahmed El-Shafie and Amr El-Shafie",
• title = "Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms",
• journal = "Sustainability",
• year = "2019",
• volume = "11",
• number = "17",
• keywords = "genetic algorithms, genetic programming, sea level prediction, monthly mean sea level prediction, east coast of Peninsular Malaysia, support vector machine, SVM",
• ISSN = "2071-1050",
• URL = "https://www.mdpi.com/2071-1050/11/17/4643",
• DOI = "doi:10.3390/su11174643",
• abstract = "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 generalised 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.",
• notes = "also known as \cite{su11174643}",

}

Genetic Programming entries for Vivien Lai Al Mahfoodh Ali Najah Ahmed Marlinda Binti Abdul Malek Haitham Abdulmohsin Afan Rusul Khaleel Ibrahim Ahmed Hussein Kamel Ahmed Elshafie Amr El-Shafie

Citations