Bahari, Nur Amira Afiza Bt Saiful and Ahmed, Ali Najah and Chong, Kai Lun and Lai, Vivien and Huang, Yuk Feng and Koo, Chai Hoon and Ng, Jing Lin and El-Shafie, Ahmed (2023) Predicting Sea Level Rise Using Artificial Intelligence: A Review. Archives of Computational Methods In Engineering, 30 (7). pp. 4045-4062. ISSN 1134-3060, DOI https://doi.org/10.1007/s11831-023-09934-9.
Full text not available from this repository.Abstract
Forecasting sea level is critical for coastal structure building and port operations. There are, however, challenges in making these predictions, resulting from the complicated processes at various periods. This study discussed the continual development of the application and forecasting approaches for sea level rise, in standard and advanced modeling versions. To date, the tide gauge and satellite altimetry are the commonly used approaches for sea level measurement. Tide gauges are mostly deficient in typical offshore circumstances; but however, this may be compensated for with satellite altimetry, a complementing technique. With technological improvement, sea level measurement may be forecasted using a variety of computer science approaches known as artificial intelligence, including machine learning and deep learning; capable of extracting information and formulating relationships from the given dataset. Its potential and extensive advantages led to a sharp growth in its recognition among hydrologists. The most successful techniques for enhancing these approaches include hybridization, ensemble modeling, data decomposition, and algorithm optimization. These advanced techniques are a prominent study area and a viable strategy for determining intelligent forecasts of sea level rise with sufficient lead time. For improved performance, the modeling requires incorporating numerous input parameters, such as precipitation, wind direction, ocean current, and sea surface temperature; for better representing the process, thus reducing forecast error and uncertainty. Deep learning is more effective and enhances existing machine learning models for forecasting future sea level rise due to its automatic feature extraction and memory-storing capability.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Tunku Abdul Rahman (UTAR), Malaysia, via Project Research Assistantship (UTARRPS 6251/H03) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Sep 2025 07:36 |
| Last Modified: | 30 Sep 2025 07:36 |
| URI: | http://eprints.um.edu.my/id/eprint/50331 |
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