A review on deep learning approaches to forecasting the changes of sea level

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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Keyword:	CNN , Deep learning , GRU , LSTM , Non-astronomical , Sea level trend
Subjects:	G Geography. Anthropology. Recreation > GC Oceanography Q Science > QA Mathematics
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	23 Jul 2021 12:25
Last Modified:	23 Jul 2021 16:50
URI:	https://eprints.ums.edu.my/id/eprint/30006

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