Modelling of sea water level during high tide using statistical method and neural network
Recently, the rise of sea level has caused an increase in rising tides that affected about three million locations around the world. The tide rising phenomenon has been occurring in Peninsular Malaysia since the 20th century. The purpose of this study is to determine the most critical station and fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/21406/ http://journalarticle.ukm.my/21406/1/JKSI_2.pdf |
| _version_ | 1848815344143040512 |
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| author | Firdaus Mohamad Hamzah, Izamarlina Asshaari, Mohd Saifullah Rusiman, Mohd Khairul Amri Kamarudin, Shamsul Rijal Muhammad Sabri, Seen, Wong Khai |
| author_facet | Firdaus Mohamad Hamzah, Izamarlina Asshaari, Mohd Saifullah Rusiman, Mohd Khairul Amri Kamarudin, Shamsul Rijal Muhammad Sabri, Seen, Wong Khai |
| author_sort | Firdaus Mohamad Hamzah, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Recently, the rise of sea level has caused an increase in rising tides that affected about three million locations around the world. The tide rising phenomenon has been occurring in Peninsular Malaysia since the 20th century. The purpose of this study is to determine the most critical station and forecast three stations located on the West Coast of Peninsular Malaysia. The Box Plot analysis method has been used in this study; the results shown that Bagan Datuk Station is the most critical station. This is due to the maximum tide’s value of Bagan Datuk Station experienced the highest increment of 0.45 m, compared to Port Klang station and Permatang Sedepa Station with only 0.2 m increment in 10 years. However, these three stations are also experiencing rising tides. Thus, the focus of managing coastal structures should be given to all these three stations as well. In addition, for forecasting, the Artificial Neural Network (ANN) forecasting model provides better forecasting results compared to the Autoregressive Integrated Moving Average (ARIMA) model for long-term forecast. In this study, the artificial Neural Network (ANN) forecasting model obtained value of RMSE 0.05642 at Bagan Datuk Station compared to the RMSE value of 0.0928 obtained from the ARIMA model at the same station. Besides, MAE value of ANN method, 0.04387 compared to the MAE value of ARIMA which is worth 0.06391 at Bagan Datuk Station. This study can conclude that the Artificial Neural Network (ANN) forecasting model is better in high tide forecasting. |
| first_indexed | 2025-11-15T00:48:29Z |
| format | Article |
| id | oai:generic.eprints.org:21406 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:48:29Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:214062023-04-05T02:04:55Z http://journalarticle.ukm.my/21406/ Modelling of sea water level during high tide using statistical method and neural network Firdaus Mohamad Hamzah, Izamarlina Asshaari, Mohd Saifullah Rusiman, Mohd Khairul Amri Kamarudin, Shamsul Rijal Muhammad Sabri, Seen, Wong Khai Recently, the rise of sea level has caused an increase in rising tides that affected about three million locations around the world. The tide rising phenomenon has been occurring in Peninsular Malaysia since the 20th century. The purpose of this study is to determine the most critical station and forecast three stations located on the West Coast of Peninsular Malaysia. The Box Plot analysis method has been used in this study; the results shown that Bagan Datuk Station is the most critical station. This is due to the maximum tide’s value of Bagan Datuk Station experienced the highest increment of 0.45 m, compared to Port Klang station and Permatang Sedepa Station with only 0.2 m increment in 10 years. However, these three stations are also experiencing rising tides. Thus, the focus of managing coastal structures should be given to all these three stations as well. In addition, for forecasting, the Artificial Neural Network (ANN) forecasting model provides better forecasting results compared to the Autoregressive Integrated Moving Average (ARIMA) model for long-term forecast. In this study, the artificial Neural Network (ANN) forecasting model obtained value of RMSE 0.05642 at Bagan Datuk Station compared to the RMSE value of 0.0928 obtained from the ARIMA model at the same station. Besides, MAE value of ANN method, 0.04387 compared to the MAE value of ARIMA which is worth 0.06391 at Bagan Datuk Station. This study can conclude that the Artificial Neural Network (ANN) forecasting model is better in high tide forecasting. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/21406/1/JKSI_2.pdf Firdaus Mohamad Hamzah, and Izamarlina Asshaari, and Mohd Saifullah Rusiman, and Mohd Khairul Amri Kamarudin, and Shamsul Rijal Muhammad Sabri, and Seen, Wong Khai (2022) Modelling of sea water level during high tide using statistical method and neural network. Jurnal Kejuruteraan, 34 (SI5(2)). pp. 9-22. ISSN 0128-0198 https://www.ukm.my/jkukm/si-5-2-2022/ |
| spellingShingle | Firdaus Mohamad Hamzah, Izamarlina Asshaari, Mohd Saifullah Rusiman, Mohd Khairul Amri Kamarudin, Shamsul Rijal Muhammad Sabri, Seen, Wong Khai Modelling of sea water level during high tide using statistical method and neural network |
| title | Modelling of sea water level during high tide using statistical method and neural network |
| title_full | Modelling of sea water level during high tide using statistical method and neural network |
| title_fullStr | Modelling of sea water level during high tide using statistical method and neural network |
| title_full_unstemmed | Modelling of sea water level during high tide using statistical method and neural network |
| title_short | Modelling of sea water level during high tide using statistical method and neural network |
| title_sort | modelling of sea water level during high tide using statistical method and neural network |
| url | http://journalarticle.ukm.my/21406/ http://journalarticle.ukm.my/21406/ http://journalarticle.ukm.my/21406/1/JKSI_2.pdf |