A review on deep learning approaches to forecasting the changes of sea level
The amalgamation of atmospheric elements indicates positive trends in sea level rise which has had a significant impact on nearly 60% of the world’s population living in the low elevated coastal area. In this paper, we first discuss potential factors leading to the rise in sea level and negative imp...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer, Singapore
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/30006/1/A%20review%20on%20deep%20learning%20approaches%20to%20forecasting%20the%20changes%20of%20sea%20level-Abstract.pdf https://eprints.ums.edu.my/id/eprint/30006/3/A%20Review%20on%20Deep%20Learning%20Approaches%20to%20Forecasting%20the%20Changes%20of%20Sea%20Level.pdf |
| _version_ | 1825714280112062464 |
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| author | Nosius Luaran Rayner Alfred Joe Henry Obit Chin Kim On |
| author_facet | Nosius Luaran Rayner Alfred Joe Henry Obit Chin Kim On |
| author_sort | Nosius Luaran |
| collection | UMS |
| description | The amalgamation of atmospheric elements indicates positive trends in sea level rise which has had a significant impact on nearly 60% of the world’s population living in the low elevated coastal area. In this paper, we first discuss potential factors leading to the rise in sea level and negative impacts on future development along the coastal region. Then, methods of acquiring sea level data which revolutionize the study of variation at sea level will also be reviewed and discussed. The present paper aims to review several Deep Learning (DL) algorithms that address critical issues of forecasting, specifically a time variable known as time series by managing complex patterns and inefficiently capturing long-term multivariate data dependency. Asynchronous data handling required correct theoretical framework processes. Based on the review conducted, the deep learning architecture is capable of generating accurate prediction at sea level which can be used as decision-making tools for managing low-lying coastal areas. |
| first_indexed | 2024-03-06T03:09:27Z |
| format | Conference or Workshop Item |
| id | ums.eprints-30006 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:09:27Z |
| publishDate | 2021 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | ums.eprints-300062021-07-23T08:50:34Z https://eprints.ums.edu.my/id/eprint/30006/ A review on deep learning approaches to forecasting the changes of sea level Nosius Luaran Rayner Alfred Joe Henry Obit Chin Kim On GC Oceanography QA Mathematics The amalgamation of atmospheric elements indicates positive trends in sea level rise which has had a significant impact on nearly 60% of the world’s population living in the low elevated coastal area. In this paper, we first discuss potential factors leading to the rise in sea level and negative impacts on future development along the coastal region. Then, methods of acquiring sea level data which revolutionize the study of variation at sea level will also be reviewed and discussed. The present paper aims to review several Deep Learning (DL) algorithms that address critical issues of forecasting, specifically a time variable known as time series by managing complex patterns and inefficiently capturing long-term multivariate data dependency. Asynchronous data handling required correct theoretical framework processes. Based on the review conducted, the deep learning architecture is capable of generating accurate prediction at sea level which can be used as decision-making tools for managing low-lying coastal areas. Springer, Singapore 2021-03-16 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30006/1/A%20review%20on%20deep%20learning%20approaches%20to%20forecasting%20the%20changes%20of%20sea%20level-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/30006/3/A%20Review%20on%20Deep%20Learning%20Approaches%20to%20Forecasting%20the%20Changes%20of%20Sea%20Level.pdf Nosius Luaran and Rayner Alfred and Joe Henry Obit and Chin Kim On (2021) A review on deep learning approaches to forecasting the changes of sea level. In: International Conference on Computational Science and Technology, ICCST 2020, 29 - 30 August 2020, Pattaya, Thailand. https://www.springerprofessional.de/en/a-review-on-deep-learning-approaches-to-forecasting-the-changes-/18968422 |
| spellingShingle | GC Oceanography QA Mathematics Nosius Luaran Rayner Alfred Joe Henry Obit Chin Kim On A review on deep learning approaches to forecasting the changes of sea level |
| title | A review on deep learning approaches to forecasting the changes of sea level |
| title_full | A review on deep learning approaches to forecasting the changes of sea level |
| title_fullStr | A review on deep learning approaches to forecasting the changes of sea level |
| title_full_unstemmed | A review on deep learning approaches to forecasting the changes of sea level |
| title_short | A review on deep learning approaches to forecasting the changes of sea level |
| title_sort | review on deep learning approaches to forecasting the changes of sea level |
| topic | GC Oceanography QA Mathematics |
| url | https://eprints.ums.edu.my/id/eprint/30006/1/A%20review%20on%20deep%20learning%20approaches%20to%20forecasting%20the%20changes%20of%20sea%20level-Abstract.pdf https://eprints.ums.edu.my/id/eprint/30006/3/A%20Review%20on%20Deep%20Learning%20Approaches%20to%20Forecasting%20the%20Changes%20of%20Sea%20Level.pdf |
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