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

Vivien Lai,Haitham Abdulmohsin Afan,Rusul Khaleel Ibrahim,Amr El-Shafie,Ali Najah Ahmed,Ahmed El-Shafie,M.A Malek

doi:10.3390/su11174643

Vivien Lai, Haitham Abdulmohsin Afan + Show 5 more

Open Access

https://doi.org/10.3390/su11174643

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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 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.

Highlights

An increase in sea level will considerably impact the low-lying coastal regions and increase the risk of floods [1,2,3,4]
The comparison between the support vector machine (SVM) and genetic programming (GP) was further introduced in terms of accuracy improvements and average error percentage to assess the effect of the selected input presented in the results section
This study aimed to examine the capability of the SVM and GP models in different prediction horizons of 1, 5, 10, 20, and 40 years for MMSL forecasting of the sea level in Kerteh, the Tioman Island, and Tanjung Sedili at the EPCM

Summary

Introduction

An increase in sea level will considerably impact the low-lying coastal regions and increase the risk of floods [1,2,3,4]. The input design parameters exhibiting a high correlation coefficient were selected for the monthly mean sea level prediction in this study and at two other study locations. Both the model performances island with a tourism population. This study implemented two models—SVM and GP—with six different input design pSuasrtaaimnaebitleitrys2.0T19h,e11i,n4p64u3t design parameters exhibiting a high correlation coefficient were selected offo2r6 the monthly mean sea level prediction in this study and at two other study locations. Utilize the best input design of SVM2 and GP2 with optimum kernel function and selection, respectively, for different prediction horizons at all the study locations (e.g., 1, 5, 10, 20, and 40 years)

Data Normalization and Model Performance

Results

Model Performances of the SVM Model

Model Performances of GP