Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the mult...
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2025
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| author | Hazrin N.A Chong K.L Huang Y.F Ahmed A.N Ng J.L Koo C.H Tan K.W Sherif M El-shafie A |
| author_facet | Hazrin N.A Chong K.L Huang Y.F Ahmed A.N Ng J.L Koo C.H Tan K.W Sherif M El-shafie A |
| author_sort | Hazrin N.A |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. © 2023 |
| format | Resource Types::text::Article |
| id | my.uniten.dspace-37696 |
| institution | Universiti Tenaga Nasional |
| language | en_US |
| publishDate | 2025 |
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| spelling | my.uniten.dspace-376962025-04-17T16:10:01Z Predicting sea levels using ML algorithms in selected locations along coastal Malaysia Hazrin N.A Chong K.L Huang Y.F Ahmed A.N Ng J.L Koo C.H Tan K.W Sherif M El-shafie A In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. Data compiled from 1985 to 2018 was utilized for training and testing the developed models. An assessment of the multiple statistics-driven regression algorithms resulted such that each tested location was associated with a particular preferred model. The following were the developed best models for their respective study areas: In Peninsular Malaysia, the interactions linear regression model was the best at Pulau Langkawi (RMSE = 19.066), the Matern 5/2 gaussian process regression model at Geting (RMSE = 49.891), and the trilayered artificial neural network at Pulau Pinang (RMSE = 20.026), while the linear regression model was the best at Sandakan in Sabah, East Malaysia (RMSE = 14.054). Other metrics, such as MAE and R-square, were also at their best values, each providing its best values, further substantiating the RMSE respectively, at each of the study areas. These empirical statistics (or metrics) also revealed that despite employing sea level as the sole parameter, results obtained were exceptional better when utilizing a 7-day lag, regardless of the model used. Notably, lag variables with less than a 7-day lag could degrade the model's accuracy in representing ground reality. The study emphasizes the importance of thorough training and testing of ML to aid decision-makers in developing mitigation actions for the climate change phenomena of sea level rise through reliable ML. © 2023 2025-04-17T08:10:01Z 2025-04-17T08:10:01Z 2023 Resource Types::text::Article 2405-8440 10.1016/j.heliyon.2023.e19426 https://irepository.uniten.edu.my/handle/123456789/37696 en_US Heliyon |
| spellingShingle | Hazrin N.A Chong K.L Huang Y.F Ahmed A.N Ng J.L Koo C.H Tan K.W Sherif M El-shafie A Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title | Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title_full | Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title_fullStr | Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title_full_unstemmed | Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title_short | Predicting sea levels using ML algorithms in selected locations along coastal Malaysia |
| title_sort | predicting sea levels using ml algorithms in selected locations along coastal malaysia |
| url_provider | http://dspace.uniten.edu.my/ |