Time-Series Prediction of Sea Level Change in the East Coast of Peninsular Malaysia from the Supervised Learning Approach
Vivien Lai, Marlinda Abdul Malek, Samsuri Abdullah, Sarmad Dashti Latif, Ali Najah Ahmed
Abstract
Analyzing and predicting the rises in sea level are vital elements in oceanography and marine management especially in managing low-lying coastal areas.The present study aims to analyze the ability of machine learning algorithm viz.regression support vector machine (RSVM) in predicting the changes in the sea level on the east coast of Peninsular Malaysia.The selected inputs for the proposed model are monthly mean sea level (MMSL), monthly sea surface temperature (SST), rainfall and mean cloud cover (MCC) for the period from January 2007 to December 2017.A total of 132 data points for each meteorological parameter were used, where 92 (70%) data points from January 2007 to December 2015 were used for training and 40 (30%) data points from January 2016 to December 2017 were used for validating and testing.Results showed based on the correlation coefficient that the model predicts the sea level rises accurately (R= 0.861, 0.825 and 0.857) for Kerteh, Tanjung Sedili, and Tioman Island, respectively.Moreover, the predicted values were similar to the historical tide-gauge data with very low error, which indicates that the proposed RSVM model can be a promising tool for decisionmakers and can be reliable to predict monthly mean sea level rises in Malaysia.